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 751–775 of 3073 papers

TitleStatusHype
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks—0
Active Learning of Mealy Machines with Timers—0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning—0
A Structured Perspective of Volumes on Active Learning—0
基於多模態主動式學習法進行需備標記樣本之挑選用於候用校長評鑑之自動化評分系統建置(A Multimodal Active Learning Approach toward Identifying Samples to Label during the Development of Automatic Oral Presentation Assessment System for Pre-service Principals Certification Program)[In Chinese]—0
Active Learning of Multi-Index Function Models—0
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Applied metamodelling for ATM performance simulations—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Active Learning of Piecewise Gaussian Process Surrogates—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
A Pre-trained Data Deduplication Model based on Active Learning—0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
A quantum active learning algorithm for sampling against adversarial attacks—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
ALEVS: Active Learning by Statistical Leverage Sampling—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