SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2601–2650 of 3073 papers

TitleStatusHype
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Pool-Based Sequential Active Learning for RegressionCode0
Textual Membership QueriesCode0
Efficient active learning of sparse halfspaces—0
Bayesian active learning for choice models with deep Gaussian processes—0
modAL: A modular active learning framework for PythonCode0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—0
Active Learning for Breast Cancer Identification—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Active Learning for Efficient Testing of Student Programs—0
Derivative free optimization via repeated classificationCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Active covariance estimation by random sub-sampling of variables—0
Active Metric Learning for Supervised Classification—0
Supervising Feature Influence—0
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection—0
Handling Adversarial Concept Drift in Streaming Data—0
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval—0
Structural query-by-committee—0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach—0
Differentiable Submodular Maximization—0
Deep Bayesian Active Semi-Supervised LearningCode0
Active model learning and diverse action sampling for task and motion planningCode0
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active LearningCode0
Best Arm Identification for Contaminated Bandits—0
Active Learning with Logged Data—0
Active Learning with Partial FeedbackCode0
Distributional Term Set Expansion—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
Thompson Sampling for Dynamic Pricing—0
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings—0
Greedy Active Learning Algorithm for Logistic Regression Models—0
Improving Active Learning in Systematic Reviews—0
Less is more: sampling chemical space with active learningCode0
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection—0
Impact of Batch Size on Stopping Active Learning for Text Classification—0
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Efficient Test Collection Construction via Active LearningCode0
Localization-Aware Active Learning for Object Detection—0
Active Community Detection with Maximal Expected Model Change—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Few-Shot Learning with Graph Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified