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 2176–2200 of 3073 papers

TitleStatusHype
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
Bayesian Active Learning for Semantic Segmentation—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Active Learning for WBAN-based Health Monitoring—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms—0
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning—0
Extending AALpy with Passive Learning: A Generalized State-Merging Approach—0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
Coupled reaction and diffusion governing interface evolution in solid-state batteries—0
A Bayesian Active Learning Approach to Comparative Judgement—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
A Benchmark and Comparison of Active Learning for Logistic Regression—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Accelerating Batch Active Learning Using Continual Learning Techniques—0
Accelerating Battery Material Optimization through iterative Machine Learning—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Fast Rates in Pool-Based Batch Active Learning—0
ACIL: Active Class Incremental Learning for Image Classification—0
A Comparison of Strategies for Source-Free Domain Adaptation—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