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 2601–2625 of 3073 papers

TitleStatusHype
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not—0
Addressing Limited Data for Textual Entailment Across Domains—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
AdjointNet: Constraining machine learning models with physics-based codes—0
A domain-decomposed VAE method for Bayesian inverse problems—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Adversarial Sampling for Active Learning—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
Adversarial Vulnerability of Active Transfer Learning—0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
A Finite-Horizon Approach to Active Level Set Estimation—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