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 1201–1250 of 3073 papers

TitleStatusHype
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores—0
Multi-task Active Learning for Pre-trained Transformer-based ModelsCode0
Active Learning for Non-Parametric Choice Models—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
Deep Surrogate of Modular Multi Pump using Active Learning—0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
Active Learning on a Programmable Photonic Quantum Processor—0
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic QuantitiesCode0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging—0
Deep Active Learning with Budget Annotation—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Unsupervised Frequent Pattern Mining for CEP—0
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Partial-Monotone Adaptive Submodular Maximization—0
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Active Pointly-Supervised Instance SegmentationCode1
Stream-based active learning with linear models—0
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
Sample Efficient Learning of Predictors that Complement HumansCode0
Distributed Safe Learning and Planning for Multi-robot Systems—0
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias—0
Plex: Towards Reliability using Pretrained Large Model Extensions—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
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image SegmentationCode1
Teaching Interactively to Learn Emotions in Natural Language—0
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Data-Efficient Learning via Minimizing Hyperspherical Energy—0
Black-box Generalization of Machine Teaching—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
Deep Active Learning for Regression Using ε-weighted Hybrid Query StrategyCode1
Cost-Sensitive Active Learning for Incomplete DataCode0
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers—0
Human-in-the-Loop Large-Scale Predictive Maintenance of WorkstationsCode2
Patient Aware Active Learning for Fine-Grained OCT Classification—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