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 5160 of 3073 papers

TitleStatusHype
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Statistical InferenceCode1
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann EstimatorsCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
SelectLLM: Can LLMs Select Important Instructions to Annotate?Code1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Querying Easily Flip-flopped Samples for Deep Active LearningCode1
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic SegmentationCode1
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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