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 26012610 of 3073 papers

TitleStatusHype
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not0
Addressing Limited Data for Textual Entailment Across Domains0
Addressing practical challenges in Active Learning via a hybrid query strategy0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections0
AdjointNet: Constraining machine learning models with physics-based codes0
A domain-decomposed VAE method for Bayesian inverse problems0
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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