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 2926–2950 of 3073 papers

TitleStatusHype
Causal Discovery and Prediction: Methods and Algorithms—0
CELEST: Federated Learning for Globally Coordinated Threat Detection—0
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation—0
Certifying One-Phase Technology-Assisted Reviews—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Challenges and Solutions for Latin Named Entity Recognition—0
Character Feature Engineering for Japanese Word Segmentation—0
Char-RNN and Active Learning for Hashtag Segmentation—0
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
Classification Committee for Active Deep Object Detection—0
Classification Tree-based Active Learning: A Wrapper Approach—0
Classifying and sorting cluttered piles of unknown objects with robots: a learning approach—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Co-active Learning to Adapt Humanoid Movement for Manipulation—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents—0
Cohort-Based Active Modality Acquisition—0
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision—0
Cold Start Active Learning Strategies in the Context of Imbalanced Classification—0
Collaborative Active Learning in Conditional Trust Environment—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