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 401–450 of 3073 papers

TitleStatusHype
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Crowd Counting With Partial Annotations in an ImageCode0
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learningCode0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
Cost-Sensitive Reference Pair Encoding for Multi-Label LearningCode0
Cost Effective Active SearchCode0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Deep Active Learning for Anchor User PredictionCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Deep Active Learning with Adaptive AcquisitionCode0
Cost-Sensitive Active Learning for Incomplete DataCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Deep Bayesian Active Learning with Image DataCode0
Continual egocentric object recognitionCode0
Cooperative Inverse Reinforcement LearningCode0
Active Learning of Spin Network ModelsCode0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Correlation Clustering with Adaptive Similarity QueriesCode0
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Active DOP: A constituency treebank annotation tool with online learningCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Active Learning with Partial FeedbackCode0
Compute-Efficient Active LearningCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Active Learning for Argument Strength EstimationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
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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