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 661670 of 3073 papers

TitleStatusHype
Towards Deep Active Learning in Avian Bioacoustics0
The Use of AI-Robotic Systems for Scientific Discovery0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
Active Learning for Fair and Stable Online Allocations0
Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions0
Large-Scale Dataset Pruning in Adversarial Training through Data Importance ExtrapolationCode0
Towards Bayesian Data Selection0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification0
Active search for Bifurcations0
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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