- 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
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“A Survey of Generalisation in Deep Reinforcement Learning”, Et Al 2021
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“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
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“Procedural Generalization by Planning With Self-Supervised World Models”, Et Al 2021
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“MetaICL: Learning to Learn In Context”, Et Al 2021
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“Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Et Al 2021
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“Meta-learning, Social Cognition and Consciousness in Brains and Machines”, Et Al 2021
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“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Et Al 2021
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“Embodied Intelligence via Learning and Evolution”, Et Al 2021
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“Replay-Guided Adversarial Environment Design”, Et Al 2021
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“Transformers Are Meta-Reinforcement Learners”, 2021
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“Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Et Al 2021
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“Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration”, Et Al 2021
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“Bootstrapped Meta-Learning”, Et Al 2021
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“The Sensory Neuron As a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, 2021
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“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
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“Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
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“Open-Ended Learning Leads to Generally Capable Agents”, Et Al 2021
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“Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Et Al 2021
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“PonderNet: Learning to Ponder”, Et Al 2021
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“Multimodal Few-Shot Learning With Frozen Language Models”, Et Al 2021
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“LHOPT: A Generalizable Approach to Learning Optimizers”, Et Al 2021
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“Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, Et Al 2021
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“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
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“Podracer Architectures for Scalable Reinforcement Learning”, Et Al 2021
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“Meta-Learning Bidirectional Update Rules”, Et Al 2021
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“Asymmetric Self-play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI Et Al 2021
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“OmniNet: Omnidirectional Representations from Transformers”, Et Al 2021
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“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
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“ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, Et Al 2021
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“Training Learned Optimizers With Randomly Initialized Learned Optimizers”, Et Al 2021
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“Evolving Reinforcement Learning Algorithms”, Co- Et Al 2021
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“Meta Pseudo Labels”, Et Al 2021
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“Meta Learning Backpropagation And Improving It”, 2020
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“Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design”, Et Al 2020
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“Scaling down Deep Learning”, 2020
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“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
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“Prioritized Level Replay”, Et Al 2020
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“Learning from the Past: Meta-Continual Learning With Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition”, Et Al 2020
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“Tasks, Stability, Architecture, and Compute: Training More Effective Learned Optimizers, and Using Them to Train Themselves”, Et Al 2020
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“Grounded Language Learning Fast and Slow”, Et Al 2020
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“Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, 2020
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“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
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“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
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“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
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“A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation”, Fernández- Et Al 2020
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“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
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“Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Et Al 2020
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“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
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“AutoML-Zero: Open Source Code for the Paper:”AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”“, Et Al 2020
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“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
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“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
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“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
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“Solving Rubik’s Cube With a Robot Hand”, OpenAI Et Al 2019
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“Solving Rubik’s Cube With a Robot Hand [blog]”, OpenAI 2019
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“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
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“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)
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“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
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“Meta Reinforcement Learning As Task Inference”, Et Al 2019
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“Meta-learning of Sequential Strategies”, Et Al 2019
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“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
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“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
“Learned Optimizers that Scale and Generalize”, 2017-03-14 (; backlinks; similar)
“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)
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“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)
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“Designing Neural Network Architectures using Reinforcement Learning”, 2016-11-07 (; backlinks; similar)
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“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)
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“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 -
https://blog.waymo.com/2020/04/using-automated-data-augmentation-to.html#google -
https://lilianweng.github.io/lil-log/2020/08/06/neural-architecture-search.html#openai -
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: