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 451–500 of 3073 papers

TitleStatusHype
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine TranslationCode0
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Deep Diffusion Processes for Active Learning of Hyperspectral ImagesCode0
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image SegmentationCode0
Generation Of Colors using Bidirectional Long Short Term Memory NetworksCode0
Continual egocentric object recognitionCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input SpaceCode0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Enhancing Semi-supervised Domain Adaptation via Effective Target LabelingCode0
Actively Discovering New Slots for Task-oriented ConversationCode0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Active Learning for Argument Strength EstimationCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Actively Learning Gaussian Process DynamicsCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
Active Learning for Entity Filtering in Microblog StreamsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Active Few-Shot Learning with FASLCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in RobotsCode0
Feedback Coding for Active LearningCode0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Few-Shot Learning with Graph Neural NetworksCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Active Learning for Abstractive Text SummarizationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino HabitatsCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Compute-Efficient Active LearningCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Confidence Estimation Using Unlabeled DataCode0
Cooperative Inverse Reinforcement LearningCode0
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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