- See Also
-
Links
- “Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Et Al 2022
- “CDCD: Continuous Diffusion for Categorical Data”, Et Al 2022
- “Query by Committee Made Real”, Gilad-Et Al 2022
- “Multi-class Active Learning for Image Classification”, Et Al 2022
- “The Power of Ensembles for Active Learning in Image Classification”, Et Al 2022
- “Weakly Supervised Structured Output Learning for Semantic Segmentation”, Et Al 2022
- “Multi-Class Active Learning by Uncertainty Sampling With Diversity Maximization”, Et Al 2022
- “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Et Al 2022
- “Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning”, Et Al 2022
- “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Et Al 2022
- “Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Et Al 2022
- “Multi-Task Self-Training for Learning General Representations”, Et Al 2021
- “Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
- “Predictive Coding: a Theoretical and Experimental Review”, Et Al 2021
- “Adapting the Function Approximation Architecture in Online Reinforcement Learning”, 2021
- “B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Et Al 2021
- “Fully General Online Imitation Learning”, Et Al 2021
- “When Do Curricula Work?”, Et Al 2020
- “Dataset Meta-Learning from Kernel Ridge-Regression”, Et Al 2020
- “Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Et Al 2020
- “Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, 2020
- “Small-GAN: Speeding Up GAN Training Using Core-sets”, Et Al 2019
- “A Deep Active Learning System for Species Identification and Counting in Camera Trap Images”, Et Al 2019
- “On Warm-Starting Neural Network Training”, 2019
- “Data Valuation Using Reinforcement Learning”, Et Al 2019
- “BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, Et Al 2019
- “Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Et Al 2019
- “ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Et Al 2019
- “End-to-End Robotic Reinforcement Learning without Reward Engineering”, Et Al 2019
- “Data Shapley: Equitable Valuation of Data for Machine Learning”, 2019
- “Learning from Dialogue After Deployment: Feed Yourself, Chatbot!”, Et Al 2019
- “Dataset Distillation”, Et Al 2018
- “The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Et Al 2018
- “Computational Mechanisms of Curiosity and Goal-directed Exploration”, Et Al 2018
- “Conditional Neural Processes”, Et Al 2018
- “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Et Al 2018
- “More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Et Al 2018
- “Fingerprint Policy Optimisation for Robust Reinforcement Learning”, Et Al 2018
- “AutoAugment: Learning Augmentation Policies from Data”, Et Al 2018
- “Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization”, Et Al 2018
- “Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Et Al 2018
- “Active Learning With Partial Feedback”, Et Al 2018
- “Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Et Al 2018
- “Less Is More: Sampling Chemical Space With Active Learning”, Et Al 2018
- “The Eighty Five Percent Rule for Optimal Learning”, Et Al 2018
- “ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, 2018
- “Learning a Generative Model for Validity in Complex Discrete Structures”, Et Al 2017
- “Learning by Asking Questions”, Et Al 2017
- “BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
- “Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Et Al 2017
- “Classification With Costly Features Using Deep Reinforcement Learning”, Et Al 2017
- “Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning”, Et Al 2017
- “Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Et Al 2017
- “Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, 2017
- “Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017
- “Interpretable Active Learning”, Et Al 2017
- “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Et Al 2017
- “A Tutorial on Thompson Sampling”, Et Al 2017
- “Learning to Learn from Noisy Web Videos”, Et Al 2017
- “Teaching Machines to Describe Images via Natural Language Feedback”, 2017
- “OHEM: Training Region-based Object Detectors With Online Hard Example Mining”, Et Al 2016
- “LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Et Al 2015
- “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015
- “Just Sort It! A Simple and Effective Approach to Active Preference Learning”, 2015
- “Minimax Analysis of Active Learning”, 2014
- “Bayesian Active Learning for Classification and Preference Learning”, Et Al 2011
- “Rates of Convergence in Active Learning”, 2011
- “The True Sample Complexity of Active Learning”, Et Al 2010
- “Active Testing for Face Detection and Localization”, 2010
- “The Wisdom of the Few: a Collaborative Filtering Approach Based on Expert Opinions from the Web”, Et Al 2009
- “Learning and Example Selection for Object and Pattern Detection”, 1995
- “Active Learning Literature Survey”
- “Brief Summary of the Panel Discussion at DL Workshop @ICML 2015”
- “Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks”
- “How a Feel-Good AI Story Went Wrong in Flint: A Machine-learning Model Showed Promising Results, but City Officials and Their Engineering Contractor Abandoned It.”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Et Al 2022
“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, 2022-12-02 ( ; similar)
“CDCD: Continuous Diffusion for Categorical Data”, Et Al 2022
“CDCD: Continuous diffusion for categorical data”, 2022-11-28 ( ; similar)
“Query by Committee Made Real”, Gilad-Et Al 2022
“Query by Committee Made Real”, 2022-11-11 (similar)
“Multi-class Active Learning for Image Classification”, Et Al 2022
“Multi-class active learning for image classification”, 2022-11-10 (similar)
“The Power of Ensembles for Active Learning in Image Classification”, Et Al 2022
“The Power of Ensembles for Active Learning in Image Classification”, 2022-11-10 ( ; similar)
“Weakly Supervised Structured Output Learning for Semantic Segmentation”, Et Al 2022
“Weakly supervised structured output learning for semantic segmentation”, 2022-11-10 (similar)
“Multi-Class Active Learning by Uncertainty Sampling With Diversity Maximization”, Et Al 2022
“Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization”, 2022-11-09 (similar)
“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Et Al 2022
“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, 2022-10-11 ( ; similar)
“Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning”, Et Al 2022
“Beyond neural scaling laws: beating power law scaling via data pruning”, 2022-06-29 ( ; backlinks; 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)
“Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Et Al 2022
“Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy”, 2022-03-15 ( ; similar)
“Multi-Task Self-Training for Learning General Representations”, Et Al 2021
“Multi-Task Self-Training for Learning General Representations”, 2021-08-25 ( ; similar)
“Dataset Distillation With Infinitely Wide Convolutional Networks”, Et Al 2021
“Dataset Distillation with Infinitely Wide Convolutional Networks”, 2021-07-27 ( ; similar)
“Predictive Coding: a Theoretical and Experimental Review”, Et Al 2021
“Predictive Coding: a Theoretical and Experimental Review”, 2021-07-27 ( ; similar)
“Adapting the Function Approximation Architecture in Online Reinforcement Learning”, 2021
“Adapting the Function Approximation Architecture in Online Reinforcement Learning”, 2021-06-17 (similar)
“B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Et Al 2021
“B-Pref: Benchmarking Preference-Based Reinforcement Learning”, 2021-06-08 ( ; similar)
“Fully General Online Imitation Learning”, Et Al 2021
“Fully General Online Imitation Learning”, 2021-02-17 (similar)
“When Do Curricula Work?”, Et Al 2020
“When Do Curricula Work?”, 2020-12-05 (similar)
“Dataset Meta-Learning from Kernel Ridge-Regression”, Et Al 2020
“Dataset Meta-Learning from Kernel Ridge-Regression”, 2020-10-30 ( ; similar)
“Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Et Al 2020
“Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics”, 2020-09-22 ( ; similar)
“Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, 2020
“Exploring Bayesian Optimization: Breaking Bayesian Optimization into small, sizeable chunks”, 2020-05-05 ( ; similar)
“Small-GAN: Speeding Up GAN Training Using Core-sets”, Et Al 2019
“Small-GAN: Speeding Up GAN Training Using Core-sets”, 2019-10-29 ( ; backlinks; similar)
“A Deep Active Learning System for Species Identification and Counting in Camera Trap Images”, Et Al 2019
“A deep active learning system for species identification and counting in camera trap images”, 2019-10-22 (similar)
“On Warm-Starting Neural Network Training”, 2019
“On Warm-Starting Neural Network Training”, 2019-10-18 ( ; similar)
“Data Valuation Using Reinforcement Learning”, Et Al 2019
“Data Valuation using Reinforcement Learning”, 2019-09-25 ( ; similar)
“BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, Et Al 2019
“BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, 2019-06-19 (similar)
“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Et Al 2019
“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, 2019-05-14 (similar; bibliography)
“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Et Al 2019
“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, 2019-04-18 ( ; similar)
“End-to-End Robotic Reinforcement Learning without Reward Engineering”, Et Al 2019
“End-to-End Robotic Reinforcement Learning without Reward Engineering”, 2019-04-16 ( ; similar)
“Data Shapley: Equitable Valuation of Data for Machine Learning”, 2019
“Data Shapley: Equitable Valuation of Data for Machine Learning”, 2019-04-05 (similar)
“Learning from Dialogue After Deployment: Feed Yourself, Chatbot!”, Et Al 2019
“Learning from Dialogue after Deployment: Feed Yourself, Chatbot!”, 2019-01-16 (similar)
“Dataset Distillation”, Et Al 2018
“Dataset Distillation”, 2018-11-27 ( ; backlinks; similar)
“The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Et Al 2018
“The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale”, 2018-11-02 ( ; similar)
“Computational Mechanisms of Curiosity and Goal-directed Exploration”, Et Al 2018
“Computational mechanisms of curiosity and goal-directed exploration”, 2018-09-07 ( ; similar)
“Conditional Neural Processes”, Et Al 2018
“Conditional Neural Processes”, 2018-07-04 (similar)
“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)
“More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Et Al 2018
“More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch”, 2018-05-28 ( ; similar)
“Fingerprint Policy Optimisation for Robust Reinforcement Learning”, Et Al 2018
“Fingerprint Policy Optimisation for Robust Reinforcement Learning”, 2018-05-27 ( ; similar)
“AutoAugment: Learning Augmentation Policies from Data”, Et Al 2018
“AutoAugment: Learning Augmentation Policies from Data”, 2018-05-24 ( ; similar; bibliography)
“Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization”, Et Al 2018
“Optimization, fast and slow: optimally switching between local and Bayesian optimization”, 2018-05-22 (similar)
“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Et Al 2018
“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications”, 2018-04-24 ( ; similar)
“Active Learning With Partial Feedback”, Et Al 2018
“Active Learning with Partial Feedback”, 2018-02-21 (similar; bibliography)
“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Et Al 2018
“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, 2018-02-03 ( ; similar)
“Less Is More: Sampling Chemical Space With Active Learning”, Et Al 2018
“Less is more: sampling chemical space with active learning”, 2018-01-28 ( ; similar)
“The Eighty Five Percent Rule for Optimal Learning”, Et Al 2018
“The Eighty Five Percent Rule for Optimal Learning”, 2018-01-27 ( ; 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)
“Learning a Generative Model for Validity in Complex Discrete Structures”, Et Al 2017
“Learning a Generative Model for Validity in Complex Discrete Structures”, 2017-12-05 (similar)
“Learning by Asking Questions”, Et Al 2017
“Learning by Asking Questions”, 2017-12-04 (similar)
“BlockDrop: Dynamic Inference Paths in Residual Networks”, Et Al 2017
“BlockDrop: Dynamic Inference Paths in Residual Networks”, 2017-11-22 ( ; backlinks; similar)
“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Et Al 2017
“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, 2017-11-21 ( ; similar)
“Classification With Costly Features Using Deep Reinforcement Learning”, Et Al 2017
“Classification with Costly Features using Deep Reinforcement Learning”, 2017-11-20 ( ; backlinks; similar)
“Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning”, Et Al 2017
“Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning”, 2017-10-19 (similar)
“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Et Al 2017
“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, 2017-09-18 ( ; backlinks; similar)
“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, 2017
“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, 2017-09-01 ( ; similar)
“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017
“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, 2017-08-01 ( ; backlinks; similar)
“Interpretable Active Learning”, Et Al 2017
“Interpretable Active Learning”, 2017-07-31 (similar)
“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Et Al 2017
“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, 2017-07-10 ( ; similar)
“A Tutorial on Thompson Sampling”, Et Al 2017
“A Tutorial on Thompson Sampling”, 2017-07-07 ( ; similar)
“Learning to Learn from Noisy Web Videos”, Et Al 2017
“Learning to Learn from Noisy Web Videos”, 2017-06-09 ( ; backlinks; similar)
“Teaching Machines to Describe Images via Natural Language Feedback”, 2017
“Teaching Machines to Describe Images via Natural Language Feedback”, 2017-06-01 ( ; backlinks; similar)
“OHEM: Training Region-based Object Detectors With Online Hard Example Mining”, Et Al 2016
“OHEM: Training Region-based Object Detectors with Online Hard Example Mining”, 2016-04-12 ( ; similar)
“LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Et Al 2015
“LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop”, 2015-06-10 ( ; backlinks; similar)
“Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015
“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, 2015-06-06 ( ; similar)
“Just Sort It! A Simple and Effective Approach to Active Preference Learning”, 2015
“Just Sort It! A Simple and Effective Approach to Active Preference Learning”, 2015-02-19 ( ; backlinks; similar)
“Minimax Analysis of Active Learning”, 2014
“Minimax Analysis of Active Learning”, 2014-10-03 (similar)
“Bayesian Active Learning for Classification and Preference Learning”, Et Al 2011
“Bayesian Active Learning for Classification and Preference Learning”, 2011-12-24 ( ; backlinks; similar)
“Rates of Convergence in Active Learning”, 2011
“Rates of convergence in active learning”, 2011-02 (similar; bibliography)
“The True Sample Complexity of Active Learning”, Et Al 2010
“The true sample complexity of active learning”, 2010-04-29 (similar; bibliography)
“Active Testing for Face Detection and Localization”, 2010
“Active Testing for Face Detection and Localization”, 2010-03-27
“The Wisdom of the Few: a Collaborative Filtering Approach Based on Expert Opinions from the Web”, Et Al 2009
“The wisdom of the few: a collaborative filtering approach based on expert opinions from the web”, 2009-07-19 (similar)
“Learning and Example Selection for Object and Pattern Detection”, 1995
“Learning and Example Selection for Object and Pattern Detection”, 1995-12-15 (similar)
“Active Learning Literature Survey”
“Brief Summary of the Panel Discussion at DL Workshop @ICML 2015”
“Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks”
“How a Feel-Good AI Story Went Wrong in Flint: A Machine-learning Model Showed Promising Results, but City Officials and Their Engineering Contractor Abandoned It.”
Wikipedia
Miscellaneous
-
https://ai.googleblog.com/2018/10/fluid-annotation-exploratory-machine.html
-
https://ai.googleblog.com/2018/10/open-sourcing-active-question.html
-
https://ai.googleblog.com/2020/10/estimating-impact-of-training-data-with.html
-
https://medium.com/pytorch/road-defect-detection-using-deep-active-learning-98d94fe854d
-
https://www.cs.ox.ac.uk/people/yarin.gal/website/blog_2248.html
Link Bibliography
-
https://arxiv.org/abs/2206.14486
: “Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning”, Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos: -
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/1905.05393
: “Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen: -
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/1802.07427
: “Active Learning With Partial Feedback”, Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan: -
https://projecteuclid.org/journals/annals-of-statistics/volume-39/issue-1/Rates-of-convergence-in-active-learning/10.1214/10-AOS843.full
: “Rates of Convergence in Active Learning”, Steve Hanneke: -
2010-balcan.pdf
: “The True Sample Complexity of Active Learning”, Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan: