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 401–450 of 3073 papers

TitleStatusHype
Active Learning for Coreference Resolution—0
Active learning using adaptable task-based prioritisation—0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Active Learning under Label Shift—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Active learning to optimise time-expensive algorithm selection—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Dialogue Simulation in Conversational Systems—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Active Learning for Contextual Search with Binary Feedbacks—0
Active Learning Solution on Distributed Edge Computing—0
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis—0
Active learning using region-based sampling—0
Active Learning for Coreference Resolution—0
Active learning using weakly supervised signals for quality inspection—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Active Learning via Regression Beyond Realizability—0
Active Learning with a Drifting Distribution—0
Active learning with biased non-response to label requests—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Expert Advice—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Learning with Importance Sampling—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning with Logged Data—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Active Learning with Multiple Kernels—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Active Learning with Oracle Epiphany—0
Active Learning for Delineation of Curvilinear Structures—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Active Learning Polynomial Threshold Functions—0
Active Learning with Rationales for Text Classification—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing 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