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 2151–2175 of 3073 papers

TitleStatusHype
Human Activity Recognition using Smartphone—0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications—0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities—0
Human-Like Active Learning: Machines Simulating the Human Learning Process—0
Human-Machine Collaboration for Fast Land Cover Mapping—0
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks—0
Hybrid Active Learning via Deep Clustering for Video Action Detection—0
Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI—0
Hyperbolic Learning with Multimodal Large Language Models—0
Towards Robust Deep Active Learning for Scientific Computing—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems—0
ICE: Rapid Information Extraction Customization for NLP Novices—0
Identifying News from Tweets—0
Identifying Wrongly Predicted Samples: A Method for Active Learning—0
Image and Video Mining through Online Learning—0
Image Classification with Deep Reinforcement Active Learning—0
Image Restoration with Point Spread Function Regularization and Active Learning—0
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition—0
Impact of Batch Size on Stopping Active Learning for Text Classification—0
Impact of Stop Sets on Stopping Active Learning for Text Classification—0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—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