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 2951–2975 of 3073 papers

TitleStatusHype
Collaborative Gaussian Processes for Preference Learning—0
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning—0
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields—0
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Combining Distant and Partial Supervision for Relation Extraction—0
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Combining self-labeling and demand based active learning for non-stationary data streams—0
Combining Self-labeling with Selective Sampling—0
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
COMET-QE and Active Learning for Low-Resource Machine Translation—0
Comments on the proof of adaptive submodular function minimization—0
Batch Selection and Communication for Active Learning with Edge Labeling—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Comparative Study of Learning Outcomes for Online Learning Platforms—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
Competition over data: how does data purchase affect users?—0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams—0
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—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