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 1651–1700 of 3073 papers

TitleStatusHype
ALLWAS: Active Learning on Language models in WASserstein space—0
Active learning for imbalanced data under cold start—0
The Application of Active Query K-Means in Text Classification—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
Zero-Round Active Learning—0
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning—0
Revisiting Uncertainty-based Query Strategies for Active Learning with TransformersCode1
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
RISAN: Robust Instance Specific Abstention NetworkCode0
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)—0
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
Near-optimal inference in adaptive linear regression—0
Matching a Desired Causal State via Shift InterventionsCode0
Knowledge Modelling and Active Learning in Manufacturing—0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes—0
Active Learning of Abstract Plan Feasibility—0
SIMILAR: Submodular Information Measures Based Active Learning In Realistic ScenariosCode1
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity—0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
TableSense: Spreadsheet Table Detection with Convolutional Neural NetworksCode1
Multi-Domain Active Learning: Literature Review and Comparative StudyCode0
TagRuler: Interactive Tool for Span-Level Data Programming by DemonstrationCode1
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
MEAL: Manifold Embedding-based Active Learning—0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection—0
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
Phrase-level Active Learning for Neural Machine Translation—0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Active and Dynamic Beam Tracking UnderStochastic Mobility—0
Active Learning for Deep Neural Networks on Edge Devices—0
Corruption Robust Active Learning—0
Quality-Aware Memory Network for Interactive Volumetric Image SegmentationCode1
Transferable Query Selection for Active Domain Adaptation—0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs—0
Heuristic Stopping Rules For Technology-Assisted Review—0
On Minimizing Cost in Legal Document Review WorkflowsCode1
An Information Retrieval Approach to Building Datasets for Hate Speech DetectionCode0
Gone Fishing: Neural Active Learning with Fisher EmbeddingsCode1
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Active Learning for Network Traffic Classification: A Technical Study—0
Semi-supervised Active Regression—0
Rare event estimation using stochastic spectral embedding—0
Coresets for Classification -- Simplified and Strengthened—0
Show:102550
← PrevPage 34 of 62Next →

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