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 2051–2100 of 3073 papers

TitleStatusHype
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
Probabilistic Deep Learning for Instance Segmentation—0
Active learning of deep surrogates for PDEs: Application to metasurface design—0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper—0
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound ImagesCode0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision—0
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification—0
Cross-Model Image Annotation Platform with Active Learning—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—0
Active Classification with Uncertainty Comparison QueriesCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Dual Adversarial Network for Deep Active Learning—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Two Stream Active Query Suggestion for Active Learning in Connectomics—0
Learning to Rank for Active Learning: A Listwise Approach—0
Is there something I'm missing? Topic Modeling in eDiscovery—0
On Deep Unsupervised Active Learning—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Fast active learning for pure exploration in reinforcement learning—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Deep Active Learning by Model Interpretability—0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Active Learning under Label Shift—0
Active Crowd Counting with Limited Supervision—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
IALE: Imitating Active Learner EnsemblesCode0
Resource Aware Multifidelity Active Learning for Efficient Optimization—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Meta-active Learning in Probabilistically-Safe Optimization—0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesCode0
The Sample Complexity of Best-k Items Selection from Pairwise ComparisonsCode0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Deep Active Learning via Open Set RecognitionCode0
Active learning of timed automata with unobservable resets—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Sampling from a k-DPP without looking at all items—0
Functional MRI applications for psychiatric disease subtyping: a review—0
Similarity Search for Efficient Active Learning and Search of Rare Concepts—0
Motor cortex mapping using active gaussian processes—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—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