- See Also
-
Links
- “BiLD: Big Little Transformer Decoder”, Et Al 2023
- “Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent As Meta-Optimizers”, Et Al 2022
- “Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Et Al 2022
- “What Learning Algorithm Is In-context Learning? Investigations With Linear Models”, Et Al 2022
- “VeLO: Training Versatile Learned Optimizers by Scaling Up”, Et Al 2022
- “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Et Al 2022
- “ProMoT: Preserving In-Context Learning Ability in Large Language Model Fine-tuning”, Et Al 2022
- “SAP: Bidirectional Language Models Are Also Few-shot Learners”, Et Al 2022
- “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Et Al 2022
- “What Can Transformers Learn In-Context? A Case Study of Simple Function Classes”, Et Al 2022
- “Few-shot Adaptation Works With UnpredicTable Data”, Et Al 2022
- “Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling”, 2022
- “Offline RL Policies Should Be Trained to Be Adaptive”, Et Al 2022
- “TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, Et Al 2022
- “Goal-Conditioned Generators of Deep Policies”, Et Al 2022
- “Prompting Decision Transformer for Few-Shot Policy Generalization”, Et Al 2022
- “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Et Al 2022
- “NOAH: Neural Prompt Search”, Et Al 2022
- “Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”, Et Al 2022
- “Towards Learning Universal Hyperparameter Optimizers With Transformers”, Et Al 2022
- “CT0: Fine-tuned Language Models Are Continual Learners”, Et Al 2022
- “Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Et Al 2022
- “Gato: A Generalist Agent”, Et Al 2022
- “Unifying Language Learning Paradigms”, Et Al 2022
- “Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Et Al 2022
- “TK-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Et Al 2022
- “What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?”, Et Al 2022
- “Effective Mutation Rate Adaptation through Group Elite Selection”, Et Al 2022
- “DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning”, Et Al 2022
- “Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs”, Et Al 2022
- “Can Language Models Learn from Explanations in Context?”, Et Al 2022
- “Auto-Lambda: Disentangling Dynamic Task Relationships”, Et Al 2022
- “In-context Learning and Induction Heads”, Et Al 2022
- “HyperPrompt: Prompt-based Task-Conditioning of Transformers”, Et Al 2022
- “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Et Al 2022
- “NeuPL: Neural Population Learning”, Et Al 2022
- “The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention”, Et Al 2022
- “Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, Et Al 2022
- “From Data to Functa: Your Data Point Is a Function and You Should Treat It like One”, Et Al 2022
- “Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”, 2022
- “Environment Generation for Zero-Shot Compositional Reinforcement Learning”, Et Al 2022
- “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Et Al 2022
- “HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Et Al 2022
- “In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Et Al 2022
- “Automated Reinforcement Learning (AutoRL): A Survey and Open Problems”, Parker-Et Al 2022
- “Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Et Al 2022
- “The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence”, Et Al 2021
- “A Mathematical Framework for Transformer Circuits”, Et Al 2021
- “PFNs: Transformers Can Do Bayesian Inference”, Et Al 2021
- “Learning to Prompt for Continual Learning”, Et Al 2021
- “How to Learn and Represent Abstractions: An Investigation Using Symbolic Alchemy”, Et Al 2021
- “Noether Networks: Meta-Learning Useful Conserved Quantities”, Et Al 2021
- “A General Language Assistant As a Laboratory for Alignment”, Et Al 2021
- “A Rational Reinterpretation of Dual-process Theories”, Et Al 2021
- “A Modern Self-Referential Weight Matrix That Learns to Modify Itself”, Et Al 2021
- “A Survey of Generalisation in Deep Reinforcement Learning”, Et Al 2021
- “Gradients Are Not All You Need”, Et Al 2021
- “An Explanation of In-context Learning As Implicit Bayesian Inference”, Et Al 2021
- “Procedural Generalization by Planning With Self-Supervised World Models”, Et Al 2021
- “MetaICL: Learning to Learn In Context”, Et Al 2021
- “Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Et Al 2021
- “Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Et Al 2021
- “T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Et Al 2021
- “Embodied Intelligence via Learning and Evolution”, Et Al 2021
- “Replay-Guided Adversarial Environment Design”, Et Al 2021
- “Transformers Are Meta-Reinforcement Learners”, 2021
- “Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Et Al 2021
- “Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration”, Et Al 2021
- “Bootstrapped Meta-Learning”, Et Al 2021
- “The Sensory Neuron As a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, 2021
- “FLAN: Finetuned Language Models Are Zero-Shot Learners”, Et Al 2021
- “The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, Et Al 2021
- “Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
- “Open-Ended Learning Leads to Generally Capable Agents”, Et Al 2021
- “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Et Al 2021
- “PonderNet: Learning to Ponder”, Et Al 2021
- “Multimodal Few-Shot Learning With Frozen Language Models”, Et Al 2021
- “LHOPT: A Generalizable Approach to Learning Optimizers”, Et Al 2021
- “Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, Et Al 2021
- “A Full-stack Accelerator Search Technique for Vision Applications”, Et Al 2021
- “Reward Is Enough”, Et Al 2021
- “Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020”, Et Al 2021
- “CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP”, Et Al 2021
- “Podracer Architectures for Scalable Reinforcement Learning”, Et Al 2021
- “Meta-Learning Bidirectional Update Rules”, Et Al 2021
- “Asymmetric Self-play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI Et Al 2021
- “OmniNet: Omnidirectional Representations from Transformers”, Et Al 2021
- “Linear Transformers Are Secretly Fast Weight Programmers”, Et Al 2021
- “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, Reynolds & 2021
- “ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, Et Al 2021
- “Training Learned Optimizers With Randomly Initialized Learned Optimizers”, Et Al 2021
- “Evolving Reinforcement Learning Algorithms”, Co-Et Al 2021
- “Meta Pseudo Labels”, Et Al 2021
- “Meta Learning Backpropagation And Improving It”, 2020
- “Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design”, Et Al 2020
- “Scaling down Deep Learning”, 2020
- “Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms”, Et Al 2020
- “Dataset Meta-Learning from Kernel Ridge-Regression”, Et Al 2020
- “MELD: Meta-Reinforcement Learning from Images via Latent State Models”, Et Al 2020
- “Meta-trained Agents Implement Bayes-optimal Agents”, Et Al 2020
- “Learning Not to Learn: Nature versus Nurture in Silico”, 2020
- “Prioritized Level Replay”, Et Al 2020
- “Learning from the Past: Meta-Continual Learning With Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition”, Et Al 2020
- “Tasks, Stability, Architecture, and Compute: Training More Effective Learned Optimizers, and Using Them to Train Themselves”, Et Al 2020
- “Grounded Language Learning Fast and Slow”, Et Al 2020
- “Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, 2020
- “Discovering Reinforcement Learning Algorithms”, Et Al 2020
- “Deep Reinforcement Learning and Its Neuroscientific Implications”, 2020
- “Meta-Learning through Hebbian Plasticity in Random Networks”, 2020
- “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Et Al 2020
- “Learning to Learn With Feedback and Local Plasticity”, Lindsey & Litwin-2020
- “Rapid Task-Solving in Novel Environments”, Et Al 2020
- “FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Et Al 2020
- “GPT-3: Language Models Are Few-Shot Learners”, Et Al 2020
- “Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search”, Et Al 2020
- “Automatic Discovery of Interpretable Planning Strategies”, Et Al 2020
- “Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks”, Et Al 2020
- “A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation”, Fernández-Et Al 2020
- “Approximate Exploitability: Learning a Best Response in Large Games”, Et Al 2020
- “Meta-Learning in Neural Networks: A Survey”, Et Al 2020
- “Designing Network Design Spaces”, Et Al 2020
- “Agent57: Outperforming the Atari Human Benchmark”, Et Al 2020
- “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Et Al 2020
- “Accelerating and Improving AlphaZero Using Population Based Training”, Et Al 2020
- “Meta-learning Curiosity Algorithms”, Et Al 2020
- “AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”, Et Al 2020
- “AutoML-Zero: Open Source Code for the Paper:”AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”“, Et Al 2020
- “Effective Diversity in Population Based Reinforcement Learning”, Parker-Et Al 2020
- “AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, 2020
- “Smooth Markets: A Basic Mechanism for Organizing Gradient-based Learners”, Et Al 2020
- “AutoML-Zero: Evolving Code That Learns”, 2020
- “Learning Neural Activations”, 2019
- “Meta-Learning without Memorization”, Et Al 2019
- “Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-to-use Procedurally-generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills”, Et Al 2019
- “Leveraging Procedural Generation to Benchmark Reinforcement Learning”, Et Al 2019
- “Increasing Generality in Machine Learning through Procedural Content Generation”, 2019
- “Optimizing Millions of Hyperparameters by Implicit Differentiation”, Et Al 2019
- “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [blog]”, Et Al 2019
- “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction”, Et Al 2019
- “Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”, Et Al 2019
- “Solving Rubik’s Cube With a Robot Hand”, OpenAI Et Al 2019
- “Solving Rubik’s Cube With a Robot Hand [blog]”, OpenAI 2019
- “Gradient Descent: The Ultimate Optimizer”, Et Al 2019
- “Multiplicative Interactions and Where to Find Them”, Et Al 2019
- “Data Valuation Using Reinforcement Learning”, Et Al 2019
- “Emergent Tool Use From Multi-Agent Autocurricula”, Et Al 2019
- “Meta-Learning With Implicit Gradients”, Et Al 2019
- “A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains”, 2019
- “AutoML: A Survey of the State-of-the-Art”, Et Al 2019
- “Metalearned Neural Memory”, Et Al 2019
- “Algorithms for Hyper-Parameter Optimization”, Et Al 2019
- “Evolving the Hearthstone Meta”, Et Al 2019
- “Meta Reinforcement Learning”, 2019
- “One Epoch Is All You Need”, 2019
- “Compositional Generalization through Meta Sequence-to-sequence Learning”, 2019
- “Risks from Learned Optimization in Advanced Machine Learning Systems”, Et Al 2019
- “ICML 2019 Notes”, 2019
- “SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers”, Et Al 2019
- “AI-GAs: AI-generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, 2019
- “Alpha MAML: Adaptive Model-Agnostic Meta-Learning”, Et Al 2019
- “Reinforcement Learning, Fast and Slow”, Et Al 2019
- “Meta Reinforcement Learning As Task Inference”, Et Al 2019
- “Meta-learning of Sequential Strategies”, Et Al 2019
- “Meta-learners’ Learning Dynamics Are unlike Learners’”, 2019
- “Ray Interference: a Source of Plateaus in Deep Reinforcement Learning”, Et Al 2019
- “AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search”, Et Al 2019
- “Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables”, Et Al 2019
- “FIGR: Few-shot Image Generation With Reptile”, 2019
- “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Et Al 2019
- “Meta-Learning Neural Bloom Filters”, Rae & Al 2019
- “Malthusian Reinforcement Learning”, Et Al 2018
- “Meta-Learning: Learning to Learn Fast”, 2018
- “An Introduction to Deep Reinforcement Learning”, Francois-Et Al 2018
- “Evolving Space-Time Neural Architectures for Videos”, Et Al 2018
- “Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Et Al 2018
- “WBE and DRL: a Middle Way of Imitation Learning from the Human Brain”, 2018
- “BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning”, Chevalier-Et Al 2018
- “Deep Reinforcement Learning”, 2018
- “Searching for Efficient Multi-Scale Architectures for Dense Image Prediction”, Et Al 2018
- “Backprop Evolution”, Et Al 2018
- “Learning Dexterous In-Hand Manipulation”, OpenAI Et Al 2018
- “Automatically Composing Representation Transformations As a Means for Generalization”, Et Al 2018
- “Human-level Performance in First-person Multiplayer Games With Population-based Deep Reinforcement Learning”, Et Al 2018
- “Guided Evolutionary Strategies: Augmenting Random Search With Surrogate Gradients”, Et Al 2018
- “RUDDER: Return Decomposition for Delayed Rewards”, Arjona-Et Al 2018
- “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Et Al 2018
- “Fingerprint Policy Optimisation for Robust Reinforcement Learning”, Et Al 2018
- “Meta-Gradient Reinforcement Learning”, Et Al 2018
- “AutoAugment: Learning Augmentation Policies from Data”, Et Al 2018
- “Prefrontal Cortex As a Meta-reinforcement Learning System”, Et Al 2018
- “Meta-Learning Update Rules for Unsupervised Representation Learning”, Et Al 2018
- “Reviving and Improving Recurrent Back-Propagation”, Et Al 2018
- “Kickstarting Deep Reinforcement Learning”, Et Al 2018
- “Some Considerations on Learning to Explore via Meta-Reinforcement Learning”, Et Al 2018
- “One Big Net For Everything”, 2018
- “Machine Theory of Mind”, Et Al 2018
- “Evolved Policy Gradients”, Et Al 2018
- “One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, Et Al 2018
- “Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces”, 2018
- “ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, 2018
- “Population Based Training of Neural Networks”, Et Al 2017
- “BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
- “Learning to Select Computations”, Et Al 2017
- “Efficient K-shot Learning With Regularized Deep Networks”, Et Al 2017
- “Online Learning of a Memory for Learning Rates”, Et Al 2017
- “Supervising Unsupervised Learning”, 2017
- “One-Shot Visual Imitation Learning via Meta-Learning”, Et Al 2017
- “Learning With Opponent-Learning Awareness”, Et Al 2017
- “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Et Al 2017
- “Stochastic Optimization With Bandit Sampling”, Et Al 2017
- “A Simple Neural Attentive Meta-Learner”, Et Al 2017
- “Reinforcement Learning for Learning Rate Control”, Et Al 2017
- “Metacontrol for Adaptive Imagination-Based Optimization”, Et Al 2017
- “Deciding How to Decide: Dynamic Routing in Artificial Neural Networks”, 2017
- “Prototypical Networks for Few-shot Learning”, Et Al 2017
- “Learned Optimizers That Scale and Generalize”, Et Al 2017
- “MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”, Et Al 2017
- “Meta Networks”, 2017
- “Optimization As a Model for Few-Shot Learning”, 2017
- “Understanding Synthetic Gradients and Decoupled Neural Interfaces”, Et Al 2017
- “Learning to Optimize Neural Nets”, 2017
- “Learning to Superoptimize Programs”, Et Al 2017
- “Discovering Objects and Their Relations from Entangled Scene Representations”, Et Al 2017
- “An Actor-critic Algorithm for Learning Rate Learning”, Et Al 2016
- “Learning to Reinforcement Learn”, Et Al 2016
- “Learning to Learn without Gradient Descent by Gradient Descent”, Et Al 2016
- “RL2: Fast Reinforcement Learning via Slow Reinforcement Learning”, Et Al 2016
- “Designing Neural Network Architectures Using Reinforcement Learning”, Et Al 2016
- “Using Fast Weights to Attend to the Recent Past”, Et Al 2016
- “HyperNetworks”, Et Al 2016
- “Decoupled Neural Interfaces Using Synthetic Gradients”, Et Al 2016
- “Learning to Learn by Gradient Descent by Gradient Descent”, Et Al 2016
- “Matching Networks for One Shot Learning”, Et Al 2016
- “Learning to Optimize”, 2016
- “One-shot Learning With Memory-Augmented Neural Networks”, Et Al 2016
- “Adaptive Computation Time for Recurrent Neural Networks”, 2016
- “On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, 2015
- “Gradient-based Hyperparameter Optimization through Reversible Learning”, Et Al 2015
- “Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education”, 2015
- “Human-level Concept Learning through Probabilistic Program Induction”, Lake & Al 2015
- “Deep Learning in Neural Networks: An Overview”, 2014
- “Practical Bayesian Optimization of Machine Learning Algorithms”, Et Al 2012
- “Learning to Learn Using Gradient Descent”, Et Al 2001
- “On the Optimization of a Synaptic Learning Rule”, Et Al 1997
- “Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”, 1992
- “Interactions between Learning and Evolution”, 1992
- “Learning a Synaptic Learning Rule”, Et Al 1991
- “Reinforcement Learning: An Introduction §Designing Reward Signals”, 2023 (page 491)
- “The Lie Comes First, the Worlds to Accommodate It”
- “AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”
- “Prefrontal Cortex As a Meta-reinforcement Learning System [blog]”
- “Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“BiLD: Big Little Transformer Decoder”, Et Al 2023
“BiLD: Big Little Transformer Decoder”, 2023-02-15 ( ; similar)
“Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent As Meta-Optimizers”, Et Al 2022
“Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers”, 2022-12-20 ( ; similar)
“Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Et Al 2022
“Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor”, 2022-12-19 ( ; similar)
“What Learning Algorithm Is In-context Learning? Investigations With Linear Models”, Et Al 2022
“What learning algorithm is in-context learning? Investigations with linear models”, 2022-11-28 ( ; similar)
“VeLO: Training Versatile Learned Optimizers by Scaling Up”, Et Al 2022
“VeLO: Training Versatile Learned Optimizers by Scaling Up”, 2022-11-17 ( ; similar)
“BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Et Al 2022
“BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, 2022-11-03 ( ; similar)
“ProMoT: Preserving In-Context Learning Ability in Large Language Model Fine-tuning”, Et Al 2022
“ProMoT: Preserving In-Context Learning ability in Large Language Model Fine-tuning”, 2022-11-01 ( ; similar)
“SAP: Bidirectional Language Models Are Also Few-shot Learners”, Et Al 2022
“SAP: Bidirectional Language Models Are Also Few-shot Learners”, 2022-09-29 ( ; similar; bibliography)
“AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Et Al 2022
“AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, 2022-08-02 ( ; similar; bibliography)
“What Can Transformers Learn In-Context? A Case Study of Simple Function Classes”, Et Al 2022
“What Can Transformers Learn In-Context? A Case Study of Simple Function Classes”, 2022-08-01 ( ; backlinks; similar; bibliography)
“Few-shot Adaptation Works With UnpredicTable Data”, Et Al 2022
“Few-shot Adaptation Works with UnpredicTable Data”, 2022-08-01 ( ; similar)
“Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling”, 2022
“Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling”, 2022-07-09 ( ; similar)
“Offline RL Policies Should Be Trained to Be Adaptive”, Et Al 2022
“Offline RL Policies Should be Trained to be Adaptive”, 2022-07-05 ( ; similar)
“TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, Et Al 2022
“TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, 2022-07-05 ( ; backlinks; similar; bibliography)
“Goal-Conditioned Generators of Deep Policies”, Et Al 2022
“Goal-Conditioned Generators of Deep Policies”, 2022-07-04 ( ; similar)
“Prompting Decision Transformer for Few-Shot Policy Generalization”, Et Al 2022
“Prompting Decision Transformer for Few-Shot Policy Generalization”, 2022-06-27 ( ; similar; bibliography)
“RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Et Al 2022
“RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt”, 2022-06-14 ( ; similar; bibliography)
“NOAH: Neural Prompt Search”, Et Al 2022
“NOAH: Neural Prompt Search”, 2022-06-09 ( ; similar)
“Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”, Et Al 2022
“Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline (3RL)”, 2022-05-28 ( ; similar)
“Towards Learning Universal Hyperparameter Optimizers With Transformers”, Et Al 2022
“Towards Learning Universal Hyperparameter Optimizers with Transformers”, 2022-05-26 ( ; similar; bibliography)
“CT0: Fine-tuned Language Models Are Continual Learners”, Et Al 2022
“CT0: Fine-tuned Language Models are Continual Learners”, 2022-05-24 ( ; similar; bibliography)
“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Et Al 2022
“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, 2022-05-22 ( ; similar)
“Gato: A Generalist Agent”, Et Al 2022
“Gato: A Generalist Agent”, 2022-05-12 ( ; similar; bibliography)
“Unifying Language Learning Paradigms”, Et Al 2022
“Unifying Language Learning Paradigms”, 2022-05-10 ( ; similar; bibliography)
“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Et Al 2022
“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, 2022-04-22 ( ; similar)
“TK-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Et Al 2022
“Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, 2022-04-16 ( ; backlinks; similar; bibliography)
“What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?”, Et Al 2022
“What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?”, 2022-04-12 ( ; backlinks; similar)
“Effective Mutation Rate Adaptation through Group Elite Selection”, Et Al 2022
“Effective Mutation Rate Adaptation through Group Elite Selection”, 2022-04-11 ( ; similar)
“DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning”, Et Al 2022
“DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning”, 2022-04-10 ( ; similar)
“Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs”, Et Al 2022
“Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs”, 2022-04-09 (similar)
“Can Language Models Learn from Explanations in Context?”, Et Al 2022
“Can language models learn from explanations in context?”, 2022-04-05 ( ; similar)
“Auto-Lambda: Disentangling Dynamic Task Relationships”, Et Al 2022
“Auto-Lambda: Disentangling Dynamic Task Relationships”, 2022-04-04 (similar)
“In-context Learning and Induction Heads”, Et Al 2022
“In-context Learning and Induction Heads”, 2022-03-08 ( )
“HyperPrompt: Prompt-based Task-Conditioning of Transformers”, Et Al 2022
“HyperPrompt: Prompt-based Task-Conditioning of Transformers”, 2022-03-01 ( ; similar; bibliography)
“Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Et Al 2022
“Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, 2022-02-25 ( ; similar; bibliography)
“NeuPL: Neural Population Learning”, Et Al 2022
“NeuPL: Neural Population Learning”, 2022-02-15 ( ; similar; bibliography)
“The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention”, Et Al 2022
“The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention”, 2022-02-11 ( ; similar)
“Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, Et Al 2022
“Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, 2022-02-06 ( ; similar)
“From Data to Functa: Your Data Point Is a Function and You Should Treat It like One”, Et Al 2022
“From data to functa: Your data point is a function and you should treat it like one”, 2022-01-28 ( ; similar)
“Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”, 2022
“Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”, 2022-01-21 ( ; similar)
“Environment Generation for Zero-Shot Compositional Reinforcement Learning”, Et Al 2022
“Environment Generation for Zero-Shot Compositional Reinforcement Learning”, 2022-01-21 ( ; similar)
“Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Et Al 2022
“Learning robust perceptive locomotion for quadrupedal robots in the wild”, 2022-01-19 ( ; backlinks; similar; bibliography)
“HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Et Al 2022
“HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, 2022-01-11 ( ; similar)
“In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Et Al 2022
“In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, 2022-01-11 ( ; similar)
“Automated Reinforcement Learning (AutoRL): A Survey and Open Problems”, Parker-Et Al 2022
“Automated Reinforcement Learning (AutoRL): A Survey and Open Problems”, 2022-01-11 ( ; similar)
“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Et Al 2022
“Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, 2022-01-03 ( ; similar)
“The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence”, Et Al 2021
“The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence”, 2021-12-24 (similar)
“A Mathematical Framework for Transformer Circuits”, Et Al 2021
“A Mathematical Framework for Transformer Circuits”, 2021-12-22 ( )
“PFNs: Transformers Can Do Bayesian Inference”, Et Al 2021
“PFNs: Transformers Can Do Bayesian Inference”, 2021-12-20 ( ; backlinks; similar; bibliography)
“Learning to Prompt for Continual Learning”, Et Al 2021
“Learning to Prompt for Continual Learning”, 2021-12-16 ( ; similar)
“How to Learn and Represent Abstractions: An Investigation Using Symbolic Alchemy”, Et Al 2021
“How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy”, 2021-12-14 ( ; similar)
“Noether Networks: Meta-Learning Useful Conserved Quantities”, Et Al 2021
“Noether Networks: Meta-Learning Useful Conserved Quantities”, 2021-12-06 ( ; similar)
“A General Language Assistant As a Laboratory for Alignment”, Et Al 2021
“A General Language Assistant as a Laboratory for Alignment”, 2021-12-01 ( ; similar; bibliography)
“A Rational Reinterpretation of Dual-process Theories”, Et Al 2021
“A rational reinterpretation of dual-process theories”, 2021-12-01 ( ; similar)
“A Modern Self-Referential Weight Matrix That Learns to Modify Itself”, Et Al 2021
“A Modern Self-Referential Weight Matrix That Learns to Modify Itself”, 2021-11-30 (similar)
“A Survey of Generalisation in Deep Reinforcement Learning”, Et Al 2021
“A Survey of Generalisation in Deep Reinforcement Learning”, 2021-11-18 (similar)
“Gradients Are Not All You Need”, Et Al 2021
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“An Explanation of In-context Learning As Implicit Bayesian Inference”, Et Al 2021
“An Explanation of In-context Learning as Implicit Bayesian Inference”, 2021-11-03 ( ; backlinks; similar)
“Procedural Generalization by Planning With Self-Supervised World Models”, Et Al 2021
“Procedural Generalization by Planning with Self-Supervised World Models”, 2021-11-02 ( ; similar; bibliography)
“MetaICL: Learning to Learn In Context”, Et Al 2021
“MetaICL: Learning to Learn In Context”, 2021-10-29 ( ; similar)
“Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Et Al 2021
“Shaking the foundations: delusions in sequence models for interaction and control”, 2021-10-20 ( ; similar)
“Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Et Al 2021
“Meta-learning, social cognition and consciousness in brains and machines”, 2021-10-18 ( ; backlinks; similar)
“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Et Al 2021
“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, 2021-10-15 ( ; backlinks; similar)
“Embodied Intelligence via Learning and Evolution”, Et Al 2021
“Embodied intelligence via learning and evolution”, 2021-10-06 ( ; backlinks; similar)
“Replay-Guided Adversarial Environment Design”, Et Al 2021
“Replay-Guided Adversarial Environment Design”, 2021-10-06 ( ; similar)
“Transformers Are Meta-Reinforcement Learners”, 2021
“Transformers are Meta-Reinforcement Learners”, 2021-10-05 ( ; similar)
“Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Et Al 2021
“Scalable Online Planning via Reinforcement Learning Fine-Tuning”, 2021-09-30 ( ; similar)
“Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration”, Et Al 2021
“Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration”, 2021-09-17 ( ; similar)
“Bootstrapped Meta-Learning”, Et Al 2021
“Bootstrapped Meta-Learning”, 2021-09-09 ( ; similar)
“The Sensory Neuron As a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, 2021
“The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, 2021-09-07 ( ; similar)
“FLAN: Finetuned Language Models Are Zero-Shot Learners”, Et Al 2021
“FLAN: Finetuned Language Models Are Zero-Shot Learners”, 2021-09-03 ( ; similar)
“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, Et Al 2021
“The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning”, 2021-08-05 ( ; similar)
“Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
“Dataset Distillation with Infinitely Wide Convolutional Networks”, 2021-07-27 ( ; similar)
“Open-Ended Learning Leads to Generally Capable Agents”, Et Al 2021
“Open-Ended Learning Leads to Generally Capable Agents”, 2021-07-27 ( ; similar)
“Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Et Al 2021
“Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability”, 2021-07-13 ( ; similar)
“PonderNet: Learning to Ponder”, Et Al 2021
“PonderNet: Learning to Ponder”, 2021-07-12 (similar)
“Multimodal Few-Shot Learning With Frozen Language Models”, Et Al 2021
“Multimodal Few-Shot Learning with Frozen Language Models”, 2021-06-25 ( ; similar)
“LHOPT: A Generalizable Approach to Learning Optimizers”, Et Al 2021
“LHOPT: A Generalizable Approach to Learning Optimizers”, 2021-06-02 ( ; similar; bibliography)
“Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, Et Al 2021
“Towards mental time travel: a hierarchical memory for reinforcement learning agents”, 2021-05-28 ( ; similar)
“A Full-stack Accelerator Search Technique for Vision Applications”, Et Al 2021
“A Full-stack Accelerator Search Technique for Vision Applications”, 2021-05-26 ( ; similar)
“Reward Is Enough”, Et Al 2021
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“Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020”, Et Al 2021
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“CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP”, Et Al 2021
“CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP”, 2021-04-18 ( ; similar)
“Podracer Architectures for Scalable Reinforcement Learning”, Et Al 2021
“Podracer architectures for scalable Reinforcement Learning”, 2021-04-13 ( ; similar; bibliography)
“Meta-Learning Bidirectional Update Rules”, Et Al 2021
“Meta-Learning Bidirectional Update Rules”, 2021-04-10 (similar)
“Asymmetric Self-play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI Et Al 2021
“Asymmetric self-play for automatic goal discovery in robotic manipulation”, 2021-03-05 ( ; similar)
“OmniNet: Omnidirectional Representations from Transformers”, Et Al 2021
“OmniNet: Omnidirectional Representations from Transformers”, 2021-03-01 ( ; similar; bibliography)
“Linear Transformers Are Secretly Fast Weight Programmers”, Et Al 2021
“Linear Transformers Are Secretly Fast Weight Programmers”, 2021-02-22 ( ; similar)
“Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, Reynolds & 2021
“Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, 2021-02-15 ( ; backlinks; similar)
“ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, Et Al 2021
“ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, 2021-01-19 ( ; similar)
“Training Learned Optimizers With Randomly Initialized Learned Optimizers”, Et Al 2021
“Training Learned Optimizers with Randomly Initialized Learned Optimizers”, 2021-01-14 (similar)
“Evolving Reinforcement Learning Algorithms”, Co-Et Al 2021
“Evolving Reinforcement Learning Algorithms”, 2021-01-08 ( ; similar)
“Meta Pseudo Labels”, Et Al 2021
“Meta Pseudo Labels”, 2021-01-05 ( ; similar; bibliography)
“Meta Learning Backpropagation And Improving It”, 2020
“Meta Learning Backpropagation And Improving It”, 2020-12-29 ( ; similar)
“Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design”, Et Al 2020
“Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design”, 2020-12-03 (backlinks; similar)
“Scaling down Deep Learning”, 2020
“Scaling down Deep Learning”, 2020-12-01 ( ; backlinks; similar; bibliography)
“Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms”, Et Al 2020
“Reverse engineering learned optimizers reveals known and novel mechanisms”, 2020-11-04 (similar)
“Dataset Meta-Learning from Kernel Ridge-Regression”, Et Al 2020
“Dataset Meta-Learning from Kernel Ridge-Regression”, 2020-10-30 ( ; similar)
“MELD: Meta-Reinforcement Learning from Images via Latent State Models”, Et Al 2020
“MELD: Meta-Reinforcement Learning from Images via Latent State Models”, 2020-10-26 ( ; similar)
“Meta-trained Agents Implement Bayes-optimal Agents”, Et Al 2020
“Meta-trained agents implement Bayes-optimal agents”, 2020-10-21 ( ; similar)
“Learning Not to Learn: Nature versus Nurture in Silico”, 2020
“Learning not to learn: Nature versus nurture in silico”, 2020-10-09 ( ; backlinks; similar)
“Prioritized Level Replay”, Et Al 2020
“Prioritized Level Replay”, 2020-10-08 (similar)
“Learning from the Past: Meta-Continual Learning With Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition”, Et Al 2020
“Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition”, 2020-10 ( ; similar)
“Tasks, Stability, Architecture, and Compute: Training More Effective Learned Optimizers, and Using Them to Train Themselves”, Et Al 2020
“Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves”, 2020-09-23 (similar)
“Grounded Language Learning Fast and Slow”, Et Al 2020
“Grounded Language Learning Fast and Slow”, 2020-09-03 ( ; similar)
“Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, 2020
“Matt Botvinick on the spontaneous emergence of learning algorithms”, 2020-08-12 ( ; backlinks; similar; bibliography)
“Discovering Reinforcement Learning Algorithms”, Et Al 2020
“Discovering Reinforcement Learning Algorithms”, 2020-07-17 (similar)
“Deep Reinforcement Learning and Its Neuroscientific Implications”, 2020
“Deep Reinforcement Learning and Its Neuroscientific Implications”, 2020-07-13 (similar)
“Meta-Learning through Hebbian Plasticity in Random Networks”, 2020
“Meta-Learning through Hebbian Plasticity in Random Networks”, 2020-07-06 ( ; similar)
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Et Al 2020
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, 2020-07-05 ( ; backlinks; similar)
“Learning to Learn With Feedback and Local Plasticity”, Lindsey & Litwin-2020
“Learning to Learn with Feedback and Local Plasticity”, 2020-06-16 ( ; backlinks; similar)
“Rapid Task-Solving in Novel Environments”, Et Al 2020
“Rapid Task-Solving in Novel Environments”, 2020-06-05 (similar)
“FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Et Al 2020
“FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining”, 2020-06-03 ( ; similar)
“GPT-3: Language Models Are Few-Shot Learners”, Et Al 2020
“GPT-3: Language Models are Few-Shot Learners”, 2020-05-28 ( ; similar)
“Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search”, Et Al 2020
“Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search”, 2020-05-27 ( ; similar)
“Automatic Discovery of Interpretable Planning Strategies”, Et Al 2020
“Automatic Discovery of Interpretable Planning Strategies”, 2020-05-24 ( ; similar)
“Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks”, Et Al 2020
“Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks”, 2020-04-29 (similar)
“A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation”, Fernández-Et Al 2020
“A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation”, 2020-04-24 ( ; similar)
“Approximate Exploitability: Learning a Best Response in Large Games”, Et Al 2020
“Approximate exploitability: Learning a best response in large games”, 2020-04-20 ( ; similar)
“Meta-Learning in Neural Networks: A Survey”, Et Al 2020
“Meta-Learning in Neural Networks: A Survey”, 2020-04-11 (similar)
“Designing Network Design Spaces”, Et Al 2020
“Designing Network Design Spaces”, 2020-03-30 ( ; similar)
“Agent57: Outperforming the Atari Human Benchmark”, Et Al 2020
“Agent57: Outperforming the Atari Human Benchmark”, 2020-03-30 ( ; similar)
“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Et Al 2020
“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions”, 2020-03-19 ( ; similar)
“Accelerating and Improving AlphaZero Using Population Based Training”, Et Al 2020
“Accelerating and Improving AlphaZero Using Population Based Training”, 2020-03-13 ( ; similar)
“Meta-learning Curiosity Algorithms”, Et Al 2020
“Meta-learning curiosity algorithms”, 2020-03-11 ( ; similar)
“AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”, Et Al 2020
“AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”, 2020-03-06 ( ; similar)
“AutoML-Zero: Open Source Code for the Paper:”AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”“, Et Al 2020
“AutoML-Zero: Open source code for the paper: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch"”, 2020-03-02 (backlinks; similar)
“Effective Diversity in Population Based Reinforcement Learning”, Parker-Et Al 2020
“Effective Diversity in Population Based Reinforcement Learning”, 2020-02-03 ( ; similar)
“AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, 2020
“AI Helps Warehouse Robots Pick Up New Tricks: Backed by machine learning luminaries, Covariant.ai’s bots can handle jobs previously needing a human touch”, 2020-01-29 ( ; similar)
“Smooth Markets: A Basic Mechanism for Organizing Gradient-based Learners”, Et Al 2020
“Smooth markets: A basic mechanism for organizing gradient-based learners”, 2020-01-14 ( ; similar)
“AutoML-Zero: Evolving Code That Learns”, 2020
“Learning Neural Activations”, 2019
“Learning Neural Activations”, 2019-12-27 ( ; similar)
“Meta-Learning without Memorization”, Et Al 2019
“Meta-Learning without Memorization”, 2019-12-09 (similar)
“Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-to-use Procedurally-generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills”, Et Al 2019
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“Leveraging Procedural Generation to Benchmark Reinforcement Learning”, Et Al 2019
“Leveraging Procedural Generation to Benchmark Reinforcement Learning”, 2019-12-03 (similar)
“Increasing Generality in Machine Learning through Procedural Content Generation”, 2019
“Increasing Generality in Machine Learning through Procedural Content Generation”, 2019-11-29 ( ; similar)
“Optimizing Millions of Hyperparameters by Implicit Differentiation”, Et Al 2019
“Optimizing Millions of Hyperparameters by Implicit Differentiation”, 2019-11-06 (similar)
“Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [blog]”, Et Al 2019
“Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [blog]”, 2019-10-29 ( ; similar)
“Learning to Predict Without Looking Ahead: World Models Without Forward Prediction”, Et Al 2019
“Learning to Predict Without Looking Ahead: World Models Without Forward Prediction”, 2019-10-29 ( ; similar)
“Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”, Et Al 2019
“Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”, 2019-10-24 ( ; backlinks; similar)
“Solving Rubik’s Cube With a Robot Hand”, OpenAI Et Al 2019
“Solving Rubik’s Cube with a Robot Hand”, 2019-10-16 ( ; similar)
“Solving Rubik’s Cube With a Robot Hand [blog]”, OpenAI 2019
“Solving Rubik’s Cube with a Robot Hand [blog]”, 2019-10-15 ( ; backlinks; similar)
“Gradient Descent: The Ultimate Optimizer”, Et Al 2019
“Gradient Descent: The Ultimate Optimizer”, 2019-09-29 (similar)
“Multiplicative Interactions and Where to Find Them”, Et Al 2019
“Multiplicative Interactions and Where to Find Them”, 2019-09-25 ( ; similar)
“Data Valuation Using Reinforcement Learning”, Et Al 2019
“Data Valuation using Reinforcement Learning”, 2019-09-25 ( ; similar)
“Emergent Tool Use From Multi-Agent Autocurricula”, Et Al 2019
“Emergent Tool Use From Multi-Agent Autocurricula”, 2019-09-17 ( ; similar)
“Meta-Learning With Implicit Gradients”, Et Al 2019
“Meta-Learning with Implicit Gradients”, 2019-09-10 (similar)
“A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains”, 2019
“A critique of pure learning and what artificial neural networks can learn from animal brains”, 2019-08-21 (backlinks; similar)
“AutoML: A Survey of the State-of-the-Art”, Et Al 2019
“AutoML: A Survey of the State-of-the-Art”, 2019-08-02 (similar)
“Metalearned Neural Memory”, Et Al 2019
“Metalearned Neural Memory”, 2019-07-23 ( ; similar)
“Algorithms for Hyper-Parameter Optimization”, Et Al 2019
“Algorithms for Hyper-Parameter Optimization”, 2019-07-16 (backlinks; similar)
“Evolving the Hearthstone Meta”, Et Al 2019
“Evolving the Hearthstone Meta”, 2019-07-02 ( ; similar)
“Meta Reinforcement Learning”, 2019
“Meta Reinforcement Learning”, 2019-06-23 ( ; similar)
“One Epoch Is All You Need”, 2019
“One Epoch Is All You Need”, 2019-06-19 ( ; backlinks; similar; bibliography)
“Compositional Generalization through Meta Sequence-to-sequence Learning”, 2019
“Compositional generalization through meta sequence-to-sequence learning”, 2019-06-12 (backlinks; similar)
“Risks from Learned Optimization in Advanced Machine Learning Systems”, Et Al 2019
“Risks from Learned Optimization in Advanced Machine Learning Systems”, 2019-06-05 ( ; backlinks; similar)
“ICML 2019 Notes”, 2019
“ICML 2019 Notes”, 2019-06 ( ; similar; bibliography)
“SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers”, Et Al 2019
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“AI-GAs: AI-generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, 2019
“AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence”, 2019-05-27 ( ; similar)
“Alpha MAML: Adaptive Model-Agnostic Meta-Learning”, Et Al 2019
“Alpha MAML: Adaptive Model-Agnostic Meta-Learning”, 2019-05-17 (similar)
“Reinforcement Learning, Fast and Slow”, Et Al 2019
“Reinforcement Learning, Fast and Slow”, 2019-05-16 ( ; similar)
“Meta Reinforcement Learning As Task Inference”, Et Al 2019
“Meta reinforcement learning as task inference”, 2019-05-15 ( ; similar)
“Meta-learning of Sequential Strategies”, Et Al 2019
“Meta-learning of Sequential Strategies”, 2019-05-08 ( ; similar)
“Meta-learners’ Learning Dynamics Are unlike Learners’”, 2019
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“Ray Interference: a Source of Plateaus in Deep Reinforcement Learning”, Et Al 2019
“Ray Interference: a Source of Plateaus in Deep Reinforcement Learning”, 2019-04-25 (similar; bibliography)
“AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search”, Et Al 2019
“AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search”, 2019-03-26 ( ; similar)
“Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables”, Et Al 2019
“Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables”, 2019-03-19 (similar)
“FIGR: Few-shot Image Generation With Reptile”, 2019
“FIGR: Few-shot Image Generation with Reptile”, 2019-01-08 ( ; backlinks; similar)
“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Et Al 2019
“Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, 2019-01-07 ( ; similar)
“Meta-Learning Neural Bloom Filters”, Rae & Al 2019
“Meta-Learning Neural Bloom Filters”, 2019 ( ; backlinks)
“Malthusian Reinforcement Learning”, Et Al 2018
“Malthusian Reinforcement Learning”, 2018-12-17 ( ; similar)
“Meta-Learning: Learning to Learn Fast”, 2018
“Meta-Learning: Learning to Learn Fast”, 2018-11-30 ( ; similar)
“An Introduction to Deep Reinforcement Learning”, Francois-Et Al 2018
“An Introduction to Deep Reinforcement Learning”, 2018-11-30 ( ; similar)
“Evolving Space-Time Neural Architectures for Videos”, Et Al 2018
“Evolving Space-Time Neural Architectures for Videos”, 2018-11-26 ( ; backlinks; similar)
“Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Et Al 2018
“Understanding and correcting pathologies in the training of learned optimizers”, 2018-10-24 ( ; backlinks; similar)
“WBE and DRL: a Middle Way of Imitation Learning from the Human Brain”, 2018
“WBE and DRL: a Middle Way of imitation learning from the human brain”, 2018-10-20 ( ; backlinks; similar)
“BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning”, Chevalier-Et Al 2018
“BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning”, 2018-10-18 (backlinks; similar)
“Deep Reinforcement Learning”, 2018
“Deep Reinforcement Learning”, 2018-10-15 ( ; similar)
“Searching for Efficient Multi-Scale Architectures for Dense Image Prediction”, Et Al 2018
“Searching for Efficient Multi-Scale Architectures for Dense Image Prediction”, 2018-09-11 ( ; backlinks; similar)
“Backprop Evolution”, Et Al 2018
“Backprop Evolution”, 2018-08-08 (backlinks; similar)
“Learning Dexterous In-Hand Manipulation”, OpenAI Et Al 2018
“Learning Dexterous In-Hand Manipulation”, 2018-08-01 ( ; similar)
“Automatically Composing Representation Transformations As a Means for Generalization”, Et Al 2018
“Automatically Composing Representation Transformations as a Means for Generalization”, 2018-07-12 ( ; backlinks; similar)
“Human-level Performance in First-person Multiplayer Games With Population-based Deep Reinforcement Learning”, Et Al 2018
“Human-level performance in first-person multiplayer games with population-based deep reinforcement learning”, 2018-07-03 ( ; similar)
“Guided Evolutionary Strategies: Augmenting Random Search With Surrogate Gradients”, Et Al 2018
“Guided evolutionary strategies: Augmenting random search with surrogate gradients”, 2018-06-26 (backlinks; similar)
“RUDDER: Return Decomposition for Delayed Rewards”, Arjona-Et Al 2018
“RUDDER: Return Decomposition for Delayed Rewards”, 2018-06-20 (similar; bibliography)
“Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Et Al 2018
“Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, 2018-06-12 ( ; backlinks; similar)
“Fingerprint Policy Optimisation for Robust Reinforcement Learning”, Et Al 2018
“Fingerprint Policy Optimisation for Robust Reinforcement Learning”, 2018-05-27 ( ; similar)
“Meta-Gradient Reinforcement Learning”, Et Al 2018
“Meta-Gradient Reinforcement Learning”, 2018-05-24 (backlinks; similar)
“AutoAugment: Learning Augmentation Policies from Data”, Et Al 2018
“AutoAugment: Learning Augmentation Policies from Data”, 2018-05-24 ( ; similar; bibliography)
“Prefrontal Cortex As a Meta-reinforcement Learning System”, Et Al 2018
“Prefrontal cortex as a meta-reinforcement learning system”, 2018-05-14 (similar)
“Meta-Learning Update Rules for Unsupervised Representation Learning”, Et Al 2018
“Meta-Learning Update Rules for Unsupervised Representation Learning”, 2018-03-31 (similar; bibliography)
“Reviving and Improving Recurrent Back-Propagation”, Et Al 2018
“Reviving and Improving Recurrent Back-Propagation”, 2018-03-16 ( ; similar)
“Kickstarting Deep Reinforcement Learning”, Et Al 2018
“Kickstarting Deep Reinforcement Learning”, 2018-03-10 ( ; similar)
“Some Considerations on Learning to Explore via Meta-Reinforcement Learning”, Et Al 2018
“Some Considerations on Learning to Explore via Meta-Reinforcement Learning”, 2018-03-03 ( ; similar)
“One Big Net For Everything”, 2018
“One Big Net For Everything”, 2018-02-24 ( ; similar)
“Machine Theory of Mind”, Et Al 2018
“Machine Theory of Mind”, 2018-02-21 ( ; similar)
“Evolved Policy Gradients”, Et Al 2018
“Evolved Policy Gradients”, 2018-02-13 ( ; similar)
“One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, Et Al 2018
“One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, 2018-02-05 ( ; similar)
“Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces”, 2018
“Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces”, 2018-01-22 (backlinks; similar)
“ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, 2018
“ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, 2018-01-03 ( ; backlinks; similar)
“Population Based Training of Neural Networks”, Et Al 2017
“Population Based Training of Neural Networks”, 2017-11-27 (similar)
“BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
“BlockDrop: Dynamic Inference Paths in Residual Networks”, 2017-11-22 ( ; backlinks; similar)
“Learning to Select Computations”, Et Al 2017
“Learning to select computations”, 2017-11-18 (backlinks; similar)
“Efficient K-shot Learning With Regularized Deep Networks”, Et Al 2017
“Efficient K-shot Learning with Regularized Deep Networks”, 2017-10-06 ( ; backlinks; similar)
“Online Learning of a Memory for Learning Rates”, Et Al 2017
“Online Learning of a Memory for Learning Rates”, 2017-09-20 ( ; backlinks; similar)
“Supervising Unsupervised Learning”, 2017
“Supervising Unsupervised Learning”, 2017-09-14 (backlinks; similar)
“One-Shot Visual Imitation Learning via Meta-Learning”, Et Al 2017
“One-Shot Visual Imitation Learning via Meta-Learning”, 2017-09-14 ( ; similar)
“Learning With Opponent-Learning Awareness”, Et Al 2017
“Learning with Opponent-Learning Awareness”, 2017-09-13 ( ; similar)
“SMASH: One-Shot Model Architecture Search through HyperNetworks”, Et Al 2017
“SMASH: One-Shot Model Architecture Search through HyperNetworks”, 2017-08-17 ( ; backlinks; similar; bibliography)
“Stochastic Optimization With Bandit Sampling”, Et Al 2017
“Stochastic Optimization with Bandit Sampling”, 2017-08-08 (backlinks; similar)
“A Simple Neural Attentive Meta-Learner”, Et Al 2017
“A Simple Neural Attentive Meta-Learner”, 2017-07-11 ( ; backlinks; similar)
“Reinforcement Learning for Learning Rate Control”, Et Al 2017
“Reinforcement Learning for Learning Rate Control”, 2017-05-31 (backlinks; similar)
“Metacontrol for Adaptive Imagination-Based Optimization”, Et Al 2017
“Metacontrol for Adaptive Imagination-Based Optimization”, 2017-05-07 (backlinks; similar)
“Deciding How to Decide: Dynamic Routing in Artificial Neural Networks”, 2017
“Deciding How to Decide: Dynamic Routing in Artificial Neural Networks”, 2017-03-17 (backlinks; similar)
“Prototypical Networks for Few-shot Learning”, Et Al 2017
“Prototypical Networks for Few-shot Learning”, 2017-03-15 ( ; backlinks; similar)
“Learned Optimizers That Scale and Generalize”, Et Al 2017
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“MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”, Et Al 2017
“MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”, 2017-03-09 (backlinks; similar)
“Meta Networks”, 2017
“Meta Networks”, 2017-03-02 ( ; backlinks; similar)
“Optimization As a Model for Few-Shot Learning”, 2017
“Optimization as a Model for Few-Shot Learning”, 2017-03-01 ( ; similar)
“Understanding Synthetic Gradients and Decoupled Neural Interfaces”, Et Al 2017
“Understanding Synthetic Gradients and Decoupled Neural Interfaces”, 2017-03-01 ( ; similar)
“Learning to Optimize Neural Nets”, 2017
“Learning to Optimize Neural Nets”, 2017-03-01 (backlinks; similar)
“Learning to Superoptimize Programs”, Et Al 2017
“Learning to superoptimize programs”, 2017-02-23 ( ; similar)
“Discovering Objects and Their Relations from Entangled Scene Representations”, Et Al 2017
“Discovering objects and their relations from entangled scene representations”, 2017-02-16 ( ; similar)
“An Actor-critic Algorithm for Learning Rate Learning”, Et Al 2016
“An Actor-critic Algorithm for Learning Rate Learning”, 2016-12-14 (backlinks; similar)
“Learning to Reinforcement Learn”, Et Al 2016
“Learning to reinforcement learn”, 2016-11-17 ( ; similar)
“Learning to Learn without Gradient Descent by Gradient Descent”, Et Al 2016
“Learning to Learn without Gradient Descent by Gradient Descent”, 2016-11-11 ( ; similar)
“RL2: Fast Reinforcement Learning via Slow Reinforcement Learning”, Et Al 2016
“RL2: Fast Reinforcement Learning via Slow Reinforcement Learning”, 2016-11-09 ( ; similar)
“Designing Neural Network Architectures Using Reinforcement Learning”, Et Al 2016
“Designing Neural Network Architectures using Reinforcement Learning”, 2016-11-07 ( ; backlinks; similar)
“Using Fast Weights to Attend to the Recent Past”, Et Al 2016
“Using Fast Weights to Attend to the Recent Past”, 2016-10-20 ( ; similar)
“HyperNetworks”, Et Al 2016
“HyperNetworks”, 2016-09-27 ( ; similar)
“Decoupled Neural Interfaces Using Synthetic Gradients”, Et Al 2016
“Decoupled Neural Interfaces using Synthetic Gradients”, 2016-08-18 ( ; similar)
“Learning to Learn by Gradient Descent by Gradient Descent”, Et Al 2016
“Learning to learn by gradient descent by gradient descent”, 2016-06-14 ( ; backlinks; similar)
“Matching Networks for One Shot Learning”, Et Al 2016
“Matching Networks for One Shot Learning”, 2016-06-13 ( ; similar)
“Learning to Optimize”, 2016
“Learning to Optimize”, 2016-06-06 ( ; backlinks; similar)
“One-shot Learning With Memory-Augmented Neural Networks”, Et Al 2016
“One-shot Learning with Memory-Augmented Neural Networks”, 2016-05-19 ( ; similar)
“Adaptive Computation Time for Recurrent Neural Networks”, 2016
“Adaptive Computation Time for Recurrent Neural Networks”, 2016-03-29 ( ; backlinks; similar)
“On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, 2015
“On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, 2015-11-30 ( ; similar)
“Gradient-based Hyperparameter Optimization through Reversible Learning”, Et Al 2015
“Gradient-based Hyperparameter Optimization through Reversible Learning”, 2015-02-11 (backlinks; similar)
“Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education”, 2015
“Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education”, 2015 ( ; backlinks; similar; bibliography)
“Human-level Concept Learning through Probabilistic Program Induction”, Lake & Al 2015
“Deep Learning in Neural Networks: An Overview”, 2014
“Deep Learning in Neural Networks: An Overview”, 2014-04-30 ( ; similar)
“Practical Bayesian Optimization of Machine Learning Algorithms”, Et Al 2012
“Practical Bayesian Optimization of Machine Learning Algorithms”, 2012-06-13 ( ; backlinks; similar)
“Learning to Learn Using Gradient Descent”, Et Al 2001
“Learning to Learn Using Gradient Descent”, 2001-08-17 ( ; backlinks; similar)
“On the Optimization of a Synaptic Learning Rule”, Et Al 1997
“On the Optimization of a Synaptic Learning Rule”, 1997 (backlinks; similar)
“Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”, 1992
“Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”, 1992 ( ; backlinks; similar)
“Interactions between Learning and Evolution”, 1992
“Interactions between Learning and Evolution”, 1992 ( ; backlinks; similar)
“Learning a Synaptic Learning Rule”, Et Al 1991
“Learning a synaptic learning rule”, 1991-07-08 ( ; backlinks; similar; bibliography)
“Reinforcement Learning: An Introduction §Designing Reward Signals”, 2023 (page 491)
“Reinforcement Learning: An Introduction §Designing Reward Signals”,
“The Lie Comes First, the Worlds to Accommodate It”
“AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”
“Prefrontal Cortex As a Meta-reinforcement Learning System [blog]”
“Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes”
Wikipedia
Miscellaneous
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http://lukemetz.com/exploring-hyperparameter-meta-loss-landscapes-with-jax/#google
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https://blog.waymo.com/2020/04/using-automated-data-augmentation-to.html#google
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https://lilianweng.github.io/lil-log/2020/08/06/neural-architecture-search.html#openai
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https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/
Link Bibliography
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https://arxiv.org/abs/2209.14500
: “SAP: Bidirectional Language Models Are Also Few-shot Learners”, Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch: -
https://arxiv.org/abs/2208.01448#amazon
: “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, : -
https://arxiv.org/abs/2208.01066
: “What Can Transformers Learn In-Context? A Case Study of Simple Function Classes”, Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory Valiant: -
https://arxiv.org/abs/2207.01848
: “TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter: -
https://arxiv.org/abs/2206.13499
: “Prompting Decision Transformer for Few-Shot Policy Generalization”, Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan: -
https://arxiv.org/abs/2206.07137
: “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, : -
https://arxiv.org/abs/2205.13320#google
: “Towards Learning Universal Hyperparameter Optimizers With Transformers”, : -
https://arxiv.org/abs/2205.12393#facebook
: “CT0: Fine-tuned Language Models Are Continual Learners”, Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan: -
https://arxiv.org/abs/2205.06175#deepmind
: “Gato: A Generalist Agent”, : -
https://arxiv.org/abs/2205.05131#google
: “Unifying Language Learning Paradigms”, : -
https://arxiv.org/abs/2204.07705
: “T<em>k< / em>-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, : -
https://arxiv.org/abs/2203.00759
: “HyperPrompt: Prompt-based Task-Conditioning of Transformers”, : -
https://arxiv.org/abs/2202.12837#facebook
: “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer: -
https://arxiv.org/abs/2202.07415#deepmind
: “NeuPL: Neural Population Learning”, Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel: -
2022-miki.pdf
: “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, Marco Hutter: -
https://arxiv.org/abs/2112.10510
: “PFNs: Transformers Can Do Bayesian Inference”, Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter: -
https://arxiv.org/abs/2112.00861#anthropic
: “A General Language Assistant As a Laboratory for Alignment”, : -
https://arxiv.org/abs/2111.01587#deepmind
: “Procedural Generalization by Planning With Self-Supervised World Models”, : -
https://arxiv.org/abs/2106.00958#openai
: “LHOPT: A Generalizable Approach to Learning Optimizers”, Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba: -
https://www.sciencedirect.com/science/article/pii/S0004370221000862#deepmind
: “Reward Is Enough”, David Silver, Satinder Singh, Doina Precup, Richard S. Sutton: -
https://arxiv.org/abs/2104.06272#deepmind
: “Podracer Architectures for Scalable Reinforcement Learning”, Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt: -
https://arxiv.org/abs/2103.01075#google
: “OmniNet: Omnidirectional Representations from Transformers”, : -
https://arxiv.org/abs/2003.10580#google
: “Meta Pseudo Labels”, Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le: -
https://greydanus.github.io/2020/12/01/scaling-down/
: “Scaling down Deep Learning”, Sam Greydanus: -
https://www.lesswrong.com/posts/Wnqua6eQkewL3bqsF/matt-botvinick-on-the-spontaneous-emergence-of-learning
: “Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, Adam Scholl: -
https://openai.com/blog/procgen-benchmark/
: “Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-to-use Procedurally-generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills”, Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman: -
https://arxiv.org/abs/1906.06669
: “One Epoch Is All You Need”, Aran Komatsuzaki: -
https://david-abel.github.io/notes/icml_2019.pdf
: “ICML 2019 Notes”, David Abel: -
https://arxiv.org/abs/1905.01320#deepmind
: “Meta-learners’ Learning Dynamics Are unlike Learners’”, Neil C. Rabinowitz: -
https://arxiv.org/abs/1904.11455#deepmind
: “Ray Interference: a Source of Plateaus in Deep Reinforcement Learning”, Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu: -
https://arxiv.org/abs/1806.07857
: “RUDDER: Return Decomposition for Delayed Rewards”, : -
https://arxiv.org/abs/1805.09501#google
: “AutoAugment: Learning Augmentation Policies from Data”, Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le: -
https://arxiv.org/abs/1804.00222#google
: “Meta-Learning Update Rules for Unsupervised Representation Learning”, Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein: -
https://arxiv.org/abs/1708.05344
: “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston: -
2015-zhu.pdf
: “Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education”, Xiaojin Zhu: -
1991-bengio.pdf
: “Learning a Synaptic Learning Rule”, Yoshua Bengio, Samy Bengio, Jocelyn Cloutier: