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 2901–2950 of 3073 papers

TitleStatusHype
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Bounded Memory Active Learning through Enriched Queries—0
Bounds on the Generalization Error in Active Learning—0
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training—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
Budgeted stream-based active learning via adaptive submodular maximization—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Buy-in-Bulk Active Learning—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
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
Can I see an Example? Active Learning the Long Tail of Attributes and Relations—0
Can Natural Language Processing Become Natural Language Coaching?—0
Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions—0
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