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 1976–2000 of 3073 papers

TitleStatusHype
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
Evaluating Zero-cost Active Learning for Object Detection—0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Events Beyond ACE: Curated Training for Events—0
Evidential uncertainties on rich labels for active learning—0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS—0
Evolving Multi-Label Fuzzy Classifier—0
Exact Sampling from Determinantal Point Processes—0
Exemplar Guided Active Learning—0
Experimental Design for Active Transductive Inference in Large Language Models—0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning—0
Experiments in Non-Coherent Post-editing—0
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience—0
Explanation-Based Attention for Semi-Supervised Deep Active Learning—0
Exploiting Context for Robustness to Label Noise in Active Learning—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
Explore-Exploit: A Framework for Interactive and Online Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—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