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 2576–2600 of 3073 papers

TitleStatusHype
Sampling and Reconstruction of Signals on Product GraphsCode0
Autonomous Wireless Systems with Artificial Intelligence—0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
Dropout-based Active Learning for Regression—0
A Practical Incremental Learning Framework For Sparse Entity ExtractionCode0
A Machine-learning framework for automatic reference-free quality assessment in MRI—0
On the Relationship between Data Efficiency and Error for Uncertainty SamplingCode0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning—0
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian ResourceCode0
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data—0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures—0
Probabilistic Model-Agnostic Meta-Learning—0
Finding Convincing Arguments Using Scalable Bayesian Preference LearningCode0
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus—0
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]Code0
The Power of Ensembles for Active Learning in Image Classification—0
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning—0
Active and Adaptive Sequential learning—0
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-OrganizationCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Deep Active Learning for Anomaly Detection—0
Distribution Aware Active Learning—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—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