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 1851–1875 of 3073 papers

TitleStatusHype
SAFARI: Safe and Active Robot Imitation Learning with Imagination—0
Safe Active Learning for Gaussian Differential Equations—0
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
Safe Exploration for Interactive Machine Learning—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
Sales Time Series Analytics Using Deep Q-Learning—0
Salutary Labeling with Zero Human Annotation—0
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions—0
Sample Complexity of Deep Active Learning—0
Sample Efficient Active Learning of Causal Trees—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Sampling Approach Matters: Active Learning for Robotic Language Acquisition—0
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature—0
Sampling from a k-DPP without looking at all items—0
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data—0
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions—0
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data—0
Scalable Active Learning for Object Detection—0
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data—0
Scaling Evidence-based Instructional Design Expertise through Large Language Models—0
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times—0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning—0
Scholar Inbox: Personalized Paper Recommendations for Scientists—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency—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