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 1251–1275 of 3073 papers

TitleStatusHype
THMA: Tencent HD Map AI System for Creating HD Map Annotations—0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
Predicting article quality scores with machine learning: The UK Research Excellence Framework—0
MAViC: Multimodal Active Learning for Video Captioning—0
Frugal Reinforcement-based Active Learning—0
Evaluating Zero-cost Active Learning for Object Detection—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Dissimilar Nodes Improve Graph Active LearningCode0
Active learning using adaptable task-based prioritisation—0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction—0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Margin-based sampling in high dimensions: When being active is less efficient than staying passive—0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification—0
ALARM: Active LeArning of Rowhammer Mitigations—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Combining Data Generation and Active Learning for Low-Resource Question AnsweringCode0
Deep Active Learning for Computer Vision: Past and Future—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Responsible Active Learning via Human-in-the-loop Peer Study—0
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
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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