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 601–625 of 3073 papers

TitleStatusHype
A Comparison of Strategies for Source-Free Domain Adaptation—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Active Learning in Symbolic Regression with Physical Constraints—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning in Recommendation Systems with Multi-level User Preferences—0
Active Learning in Physics: From 101, to Progress, and Perspective—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
ACIL: Active Class Incremental Learning for Image Classification—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Active Community Detection with Maximal Expected Model Change—0
A Machine-learning framework for automatic reference-free quality assessment in MRI—0
A Markovian Formalism for Active Querying—0
Active Learning in Gaussian Process State Space Model—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Fast Rates in Pool-Based Batch Active Learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—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