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 1001–1025 of 3073 papers

TitleStatusHype
Agnostic Active Learning Without Constraints—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
Active Learning for Speech Recognition: the Power of Gradients—0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling—0
Bayesian Active Learning for Sim-to-Real Robotic Perception—0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Buy-in-Bulk Active Learning—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Cache & Distil: Optimising API Calls to Large Language Models—0
CADET: Computer Assisted Discovery Extraction and Translation—0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
CALICO: Confident Active Learning with Integrated Calibration—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Can Active Learning Experience Be Transferred?—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Active Learning for Sound Event Detection—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Active learning for structural reliability: survey, general framework and benchmark—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