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 2076–2100 of 3073 papers

TitleStatusHype
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian Process Classification Bandits—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Gaussian Process Meta-Representations Of Neural Networks—0
Gaussian Process Models for HRTF based Sound-Source Localization and Active-Learning—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data—0
Generalized active learning and design of statistical experiments for manifold-valued data—0
Chernoff Sampling for Active Testing and Extension to Active Regression—0
Generalized Coverage for More Robust Low-Budget Active Learning—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning—0
Generative Active Learning for the Search of Small-molecule Protein Binders—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
Generative Adversarial Active Learning—0
Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions—0
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Geometric Active Learning for Segmentation of Large 3D Volumes—0
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning—0
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems—0
Geometry-Aware Adaptation for Pretrained Models—0
Geometry in Active Learning for Binary and Multi-class Image 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