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 381390 of 3073 papers

TitleStatusHype
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning0
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System0
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles0
Scholar Inbox: Personalized Paper Recommendations for Scientists0
Towards Unconstrained 2D Pose Estimation of the Human Spine0
Low Rank Learning for Offline Query OptimizationCode0
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops0
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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