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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1751–1775 of 3073 papers

TitleStatusHype
Small-Text: Active Learning for Text Classification in Python—0
Offline Preference-Based Apprenticeship Learning—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
The Application of Active Query K-Means in Text Classification—0
Active learning for imbalanced data under cold start—0
Zero-Round Active Learning—0
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
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 Manufacturing—0
Matching a Desired Causal State via Shift InterventionsCode0
Near-optimal inference in adaptive linear regression—0
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes—0
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity—0
Active Learning of Abstract Plan Feasibility—0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
Multi-Domain Active Learning: Literature Review and Comparative StudyCode0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
MEAL: Manifold Embedding-based Active Learning—0
Show:102550
← PrevPage 71 of 123Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified