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

Showing 19011910 of 3073 papers

TitleStatusHype
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling0
Sequential Adaptive Design for Jump Regression Estimation0
Sequential Design for Optimal Stopping Problems0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model0
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF0
SHEF-Lite: When Less is More for Translation Quality Estimation0
SHINRA: Structuring Wikipedia by Collaborative Contribution0
Ship Detection in SAR Images with Human-in-the-Loop0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data0
Show:102550
← PrevPage 191 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified