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 1751–1800 of 3073 papers

TitleStatusHype
Small-Text: Active Learning for Text Classification in Python—0
Offline Preference-Based Apprenticeship Learning—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
The Application of Active Query K-Means in Text Classification—0
Active learning for imbalanced data under cold start—0
Zero-Round Active Learning—0
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning—0
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
Knowledge Modelling and Active Learning in Manufacturing—0
Matching a Desired Causal State via Shift InterventionsCode0
Near-optimal inference in adaptive linear regression—0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes—0
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity—0
Active Learning of Abstract Plan Feasibility—0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
Multi-Domain Active Learning: Literature Review and Comparative StudyCode0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
MEAL: Manifold Embedding-based Active Learning—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection—0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Phrase-level Active Learning for Neural Machine Translation—0
Corruption Robust Active Learning—0
Active and Dynamic Beam Tracking UnderStochastic Mobility—0
Active Learning for Deep Neural Networks on Edge Devices—0
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
An Information Retrieval Approach to Building Datasets for Hate Speech DetectionCode0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—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
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
Targeted Active Learning for Bayesian Decision-Making—0
Coresets for Classification -- Simplified and Strengthened—0
A critical look at the current train/test split in machine learning—0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Neural Active Learning with Performance Guarantees—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—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