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 2726–2750 of 3073 papers

TitleStatusHype
Bayesian Pool-based Active Learning With Abstention Feedbacks—0
Unfolding Hidden Barriers by Active Enhanced Sampling—0
Active Learning for Graph EmbeddingCode0
Comments on the proof of adaptive submodular function minimization—0
PANFIS++: A Generalized Approach to Evolving Learning—0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow—0
Active classification with comparison queries—0
Mining Object Parts from CNNs via Active Question-Answering—0
Parsimonious Random Vector Functional Link Network for Data Streams—0
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Goal-Driven Dynamics Learning via Bayesian Optimization—0
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions—0
Episode-Based Active Learning with Bayesian Neural Networks—0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient—0
Adaptivity to Noise Parameters in Nonparametric Active Learning—0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis—0
Learning Active Learning from DataCode0
Deep Bayesian Active Learning with Image DataCode0
Active Learning for Cost-Sensitive Classification—0
Active Learning for Accurate Estimation of Linear Models—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