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 17611770 of 3073 papers

TitleStatusHype
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection0
RISAN: Robust Instance Specific Abstention NetworkCode0
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)0
Knowledge Modelling and Active Learning in Manufacturing0
Matching a Desired Causal State via Shift InterventionsCode0
Near-optimal inference in adaptive linear regression0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes0
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity0
Active Learning of Abstract Plan Feasibility0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
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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