‘active learning’ tag

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‘active learning’ tag

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“Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024

Probing the Decision Boundaries of In-context Learning in Large Language Models

“Beyond Model Collapse: Scaling Up With Synthesized Data Requires Reinforcement”, Feng et al 2024

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement

“Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge”, Strieth-Kalthoff et al 2024

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge

“Sparse Universal Transformer”, Tan et al 2023

Sparse Universal Transformer

“Skill-It! A Data-Driven Skills Framework for Understanding and Training Language Models”, Chen et al 2023

Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models

“AlpaGasus: Training A Better Alpaca With Fewer Data”, Chen et al 2023

AlpaGasus: Training A Better Alpaca with Fewer Data

“Instruction Mining: High-Quality Instruction Data Selection for Large Language Models”, Cao et al 2023

Instruction Mining: High-Quality Instruction Data Selection for Large Language Models

“No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-Based Language Models”, Kaddour et al 2023

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

“Estimating Label Quality and Errors in Semantic Segmentation Data via Any Model”, Lad & Mueller 2023

Estimating label quality and errors in semantic segmentation data via any model

“Self Expanding Neural Networks”, Mitchell et al 2023

Self Expanding Neural Networks

“DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining”, Xie et al 2023

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining

“Chatting With GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing”, Liu et al 2023

Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing

“TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Eldan & Li 2023

TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

“Q2d: Turning Questions into Dialogs to Teach Models How to Search”, Bitton et al 2023

q2d: Turning Questions into Dialogs to Teach Models How to Search

“Segment Anything”, Kirillov et al 2023

Segment Anything

“Scaling Expert Language Models With Unsupervised Domain Discovery”, Gururangan et al 2023

Scaling Expert Language Models with Unsupervised Domain Discovery

“Modern Bayesian Experimental Design”, Rainforth et al 2023

Modern Bayesian Experimental Design

“Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities”, Kirsch & Gal 2023

Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities

“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein et al 2022

Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula

“CDCD: Continuous Diffusion for Categorical Data”, Dieleman et al 2022

CDCD: Continuous diffusion for categorical data

“Query by Committee Made Real”, Gilad-Bachrach et al 2022

Query by Committee Made Real

“Weakly Supervised Structured Output Learning for Semantic Segmentation”, Vezhnevets et al 2022

Weakly supervised structured output learning for semantic segmentation

“The Power of Ensembles for Active Learning in Image Classification”, Beluch et al 2022

The Power of Ensembles for Active Learning in Image Classification

“Multi-Class Active Learning for Image Classification”, Joshi et al 2022

Multi-class active learning for image classification

“Multi-Class Active Learning by Uncertainty Sampling With Diversity Maximization”, Yang et al 2022

Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization

“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Kocsis et al 2022

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

“Detecting Label Errors in Token Classification Data”, Wang & Mueller 2022

Detecting Label Errors in Token Classification Data

“RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Mindermann et al 2022

RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

“Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang et al 2022

Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

“Multi-Task Self-Training for Learning General Representations”, Ghiasi et al 2021

Multi-Task Self-Training for Learning General Representations

“Predictive Coding: a Theoretical and Experimental Review”, Millidge et al 2021

Predictive Coding: a Theoretical and Experimental Review

“Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen et al 2021

Dataset Distillation with Infinitely Wide Convolutional Networks

“Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning”, Kirsch et al 2021

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

“Adapting the Function Approximation Architecture in Online Reinforcement Learning”, Martin & Modayil 2021

Adapting the Function Approximation Architecture in Online Reinforcement Learning

“B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Lee et al 2021

B-Pref: Benchmarking Preference-Based Reinforcement Learning

“Fully General Online Imitation Learning”, Cohen et al 2021

Fully General Online Imitation Learning

“When Do Curricula Work?”, Wu et al 2020

When Do Curricula Work?

“Dataset Meta-Learning from Kernel Ridge-Regression”, Nguyen et al 2020

Dataset Meta-Learning from Kernel Ridge-Regression

“Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Swayamdipta et al 2020

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

“BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits”, Tiwari et al 2020

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

“Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, Agnihotri & Batra 2020

Exploring Bayesian Optimization: Breaking Bayesian Optimization into small, sizeable chunks

“Small-GAN: Speeding Up GAN Training Using Core-Sets”, Sinha et al 2019

Small-GAN: Speeding Up GAN Training Using Core-sets

“A Deep Active Learning System for Species Identification and Counting in Camera Trap Images”, Norouzzadeh et al 2019

A deep active learning system for species identification and counting in camera trap images

“On Warm-Starting Neural Network Training”, Ash & Adams 2019

On Warm-Starting Neural Network Training

“Accelerating Deep Learning by Focusing on the Biggest Losers”, Jiang et al 2019

Accelerating Deep Learning by Focusing on the Biggest Losers

“Data Valuation Using Reinforcement Learning”, Yoon et al 2019

Data Valuation using Reinforcement Learning

“BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, Kirsch et al 2019

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

“BADGE: Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds”, Ash et al 2019

BADGE: Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Ho et al 2019

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

“Learning Loss for Active Learning”, Yoo & Kweon 2019

Learning Loss for Active Learning

“A Recipe for Training Neural Networks”, Karpathy 2019

A Recipe for Training Neural Networks

“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang et al 2019

ProductNet: a Collection of High-Quality Datasets for Product Representation Learning

“End-To-End Robotic Reinforcement Learning without Reward Engineering”, Singh et al 2019

End-to-End Robotic Reinforcement Learning without Reward Engineering

“Data Shapley: Equitable Valuation of Data for Machine Learning”, Ghorbani & Zou 2019

Data Shapley: Equitable Valuation of Data for Machine Learning

“Learning from Dialogue After Deployment: Feed Yourself, Chatbot!”, Hancock et al 2019

Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

“The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Kuznetsova et al 2018

The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale

“Computational Mechanisms of Curiosity and Goal-Directed Exploration”, Schwartenbeck et al 2018

Computational mechanisms of curiosity and goal-directed exploration

“Conditional Neural Processes”, Garnelo et al 2018

Conditional Neural Processes

“Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Pang et al 2018

Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning

“More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Calandra et al 2018

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

“Fingerprint Policy Optimization for Robust Reinforcement Learning”, Paul et al 2018

Fingerprint Policy Optimization for Robust Reinforcement Learning

“AutoAugment: Learning Augmentation Policies from Data”, Cubuk et al 2018

AutoAugment: Learning Augmentation Policies from Data

“Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization”, McLeod et al 2018

Optimization, fast and slow: optimally switching between local and Bayesian optimization

“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Hadash et al 2018

Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications

“Active Learning With Partial Feedback”, Hu et al 2018

Active Learning with Partial Feedback

“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Zhou et al 2018

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

“Less Is More: Sampling Chemical Space With Active Learning”, Smith et al 2018

Less is more: sampling chemical space with active learning

“The Eighty Five Percent Rule for Optimal Learning”, Wilson et al 2018

The Eighty Five Percent Rule for Optimal Learning

“ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, Kim & Choi 2018

ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

“Learning a Generative Model for Validity in Complex Discrete Structures”, Janz et al 2017

Learning a Generative Model for Validity in Complex Discrete Structures

“Learning by Asking Questions”, Misra et al 2017

Learning by Asking Questions

“BlockDrop: Dynamic Inference Paths in Residual Networks”, Wu et al 2017

BlockDrop: Dynamic Inference Paths in Residual Networks

“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Yang et al 2017

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

“Classification With Costly Features Using Deep Reinforcement Learning”, Janisch et al 2017

Classification with Costly Features using Deep Reinforcement Learning

“Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-Sensitive Learning”, Depeweg et al 2017

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Shim et al 2017

Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification

“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, Jayaraman & Grauman 2017

Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, Sener & Savarese 2017

Active Learning for Convolutional Neural Networks: A Core-Set Approach

“Interpretable Active Learning”, Phillips et al 2017

Interpretable Active Learning

“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun et al 2017

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

“A Tutorial on Thompson Sampling”, Russo et al 2017

A Tutorial on Thompson Sampling

“Learning to Learn from Noisy Web Videos”, Yeung et al 2017

Learning to Learn from Noisy Web Videos

“Teaching Machines to Describe Images via Natural Language Feedback”, Ling & Fidler 2017

Teaching Machines to Describe Images via Natural Language Feedback

“Ask the Right Questions: Active Question Reformulation With Reinforcement Learning”, Buck et al 2017

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

“BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography”, Wilber et al 2017

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

“PBO: Preferential Bayesian Optimization”, Gonzalez et al 2017

PBO: Preferential Bayesian Optimization

“OHEM: Training Region-Based Object Detectors With Online Hard Example Mining”, Shrivastava et al 2016

OHEM: Training Region-based Object Detectors with Online Hard Example Mining

“The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition”, Krause et al 2015

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

“LSUN: Construction of a Large-Scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu et al 2015

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

“Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, Gal & Ghahramani 2015

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

“Just Sort It! A Simple and Effective Approach to Active Preference Learning”, Maystre & Grossglauser 2015

Just Sort It! A Simple and Effective Approach to Active Preference Learning

“Learning With Intelligent Teacher: Similarity Control and Knowledge Transfer”, Vapnik & Izmailov 2015

Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer

“Minimax Analysis of Active Learning”, Hanneke & Yang 2014

Minimax Analysis of Active Learning

“Algorithmic and Human Teaching of Sequential Decision Tasks”, Cakmak & Lopes 2012

Algorithmic and Human Teaching of Sequential Decision Tasks

“Bayesian Active Learning for Classification and Preference Learning”, Houlsby et al 2011

Bayesian Active Learning for Classification and Preference Learning

“Rates of Convergence in Active Learning”, Hanneke 2011

Rates of convergence in active learning

“The True Sample Complexity of Active Learning”, Balcan et al 2010

The true sample complexity of active learning

“Active Testing for Face Detection and Localization”, Sznitman & Jedynak 2010

Active Testing for Face Detection and Localization

“The Wisdom of the Few: a Collaborative Filtering Approach Based on Expert Opinions from the Web”, Amatriain et al 2009

The wisdom of the few: a collaborative filtering approach based on expert opinions from the web

“Learning and Example Selection for Object and Pattern Detection”, Sung 1995

Learning and Example Selection for Object and Pattern Detection

“Information-Based Objective Functions for Active Data Selection”, MacKay 1992

Information-Based Objective Functions for Active Data Selection

“Active Learning Literature Survey”

Active Learning Literature Survey

“Brief Summary of the Panel Discussion at DL Workshop @ICML 2015”

Brief Summary of the Panel Discussion at DL Workshop @ICML 2015⁠:

View HTML:

/doc/www/kyunghyuncho.me/415a798fa1f3d6bade4858ab71ab735aad551407.html

“Active Learning”

Active Learning⁠:

View HTML:

https://lilianweng.github.io/lil-log/2022/02/20/active-learning.html

“Aurora’s Approach to Development”

Aurora’s Approach to Development

“Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks”

Active Learning for High Dimensional Inputs using Bayesian Convolutional Neural Networks⁠:

View PDF:

/doc/www/raw.githubusercontent.com/41cc1ed0e2e1ca39190300331f08f2f21aaa66ac.pdf

“AI-Guided Robots Are Ready to Sort Your Recyclables”

AI-Guided Robots Are Ready to Sort Your Recyclables

“When Self-Driving Cars Can’t Help Themselves, Who Takes the Wheel?”

When Self-Driving Cars Can’t Help Themselves, Who Takes the Wheel?

“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.”

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.

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