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 521530 of 3073 papers

TitleStatusHype
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information0
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann EstimatorsCode1
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunitiesCode0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments0
Mode Estimation with Partial Feedback0
Fairness Without Harm: An Influence-Guided Active Sampling ApproachCode0
ELAD: Explanation-Guided Large Language Models Active Distillation0
Bayesian Active Learning for Censored Regression0
Key Patch Proposer: Key Patches Contain Rich InformationCode0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning0
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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