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 1051–1075 of 3073 papers

TitleStatusHype
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Confidence Estimation for Object Detection in Document Images—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Active Learning for Regression with Aggregated Outputs—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Active Learning and CSI Acquisition for mmWave Initial Alignment—0
Active Anomaly Detection for time-domain discoveries—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Active learning for regression in engineering populations: A risk-informed approach—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
Active Learning and Best-Response Dynamics—0
Extending AALpy with Passive Learning: A Generalized State-Merging Approach—0
Confident Coreset for Active Learning in Medical Image Analysis—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Active Learning for Regression by Inverse Distance Weighting—0
A domain-decomposed VAE method for Bayesian inverse problems—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal—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