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 501–525 of 3073 papers

TitleStatusHype
Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education—0
Superposition through Active Learning lens—0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Active learning of neural population dynamics using two-photon holographic optogenetics—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
PAL -- Parallel active learning for machine-learned potentialsCode0
Neural Window Decoder for SC-LDPC Codes—0
Active partitioning: inverting the paradigm of active learning—0
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System—0
Maximally Separated Active Learning—0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
Benchmarking Active Learning for NILM—0
Influence functions and regularity tangents for efficient active learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Stream-Based Active Learning for Process Monitoring—0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
Targeting Negative Flips in Active Learning using Validation SetsCode0
Learning Quantitative Automata Modulo Theories—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