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 951–1000 of 3073 papers

TitleStatusHype
Active Model Aggregation via Stochastic Mirror Descent—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Benchmarking Active Learning for NILM—0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
Active Metric Learning from Relative Comparisons—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Active Metric Learning for Supervised Classification—0
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Best Arm Identification for Contaminated Bandits—0
Active metric learning and classification using similarity queries—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Beyond Disagreement-based Agnostic Active Learning—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Bayesian Semisupervised Learning with Deep Generative Models—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection—0
Bias-Aware Heapified Policy for Active Learning—0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Bilingual Transfer Learning for Online Product Classification—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active Few-Shot Fine-Tuning—0
Bayesian Pool-based Active Learning With Abstention Feedbacks—0
Actively Learning what makes a Discrete Sequence Valid—0
Boosting API Recommendation with Implicit Feedback—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Bayesian Nonparametric Crowdsourcing—0
Actively learning to learn causal relationships—0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Bayesian Hypernetworks—0
Bounds on the Generalization Error in Active Learning—0
Active learning for energy-based antibody optimization and enhanced screening—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
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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