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 231240 of 3073 papers

TitleStatusHype
DISCERN: Decoding Systematic Errors in Natural Language for Text ClassifiersCode0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits0
Efficient Biological Data Acquisition through Inference Set Design0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand0
regAL: Python Package for Active Learning of Regression Problems0
Learning signals defined on graphs with optimal transport and Gaussian process regression0
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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