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
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
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
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
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—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
Deep Active Learning for Computer Vision: Past and Future—0
Combining Data Generation and Active Learning for Low-Resource Question AnsweringCode0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Responsible Active Learning via Human-in-the-loop Peer Study—0
PyTAIL: Interactive and Incremental Learning of NLP Models with Human in the Loop for Online DataCode1
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
Plug and Play Active Learning for Object DetectionCode1
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—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