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 1376–1400 of 3073 papers

TitleStatusHype
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Open Long-Tailed Recognition in a Dynamic World—0
Semi-supervised Learning with Deterministic Labeling and Large Margin ProjectionCode0
Bucketized Active Sampling for Learning ACOPF—0
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning—0
Continuous Active Learning Using Pretrained Transformers—0
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
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Active Learning for Non-Parametric Choice Models—0
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
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
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
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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