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 1401–1450 of 3073 papers

TitleStatusHype
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Stream-based active learning with linear models—0
Sample Efficient Learning of Predictors that Complement HumansCode0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Plex: Towards Reliability using Pretrained Large Model Extensions—0
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias—0
Uncertainty quantification for predictions of atomistic neural networksCode0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering—0
Mitigating shortage of labeled data using clustering-based active learning with diversity explorationCode0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning—0
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Teaching Interactively to Learn Emotions in Natural Language—0
Black-box Generalization of Machine Teaching—0
Data-Efficient Learning via Minimizing Hyperspherical Energy—0
Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities—0
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels—0
Mitigating sampling bias in risk-based active learning via an EM algorithm—0
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers—0
Patient Aware Active Learning for Fine-Grained OCT Classification—0
Cost-Sensitive Active Learning for Incomplete DataCode0
Active Learning with Safety Constraints—0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
DECAL: DEployable Clinical Active Learning—0
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learningCode0
Actively learning to learn causal relationships—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Deep reinforced active learning for multi-class image classification—0
Towards Efficient Active Learning of PDFACode0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Efficient Human-in-the-loop System for Guiding DNNs AttentionCode0
Physics-informed EDFA Gain Model Based on Active Learning—0
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning—0
Weighted Ensembles for Active Learning with Adaptivity—0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning—0
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning—0
Indirect Active Learning—0
BayesFormer: Transformer with Uncertainty Estimation—0
Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Support Vector Machines under Adversarial Label Contamination—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Deep Active Learning with Noise Stability—0
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms—0
Active Labeling: Streaming Stochastic GradientsCode0
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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