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 551–600 of 3073 papers

TitleStatusHype
Active Learning of Multi-Index Function Models—0
Active Learning for Argument Mining: A Practical Approach—0
Active Deep Decoding of Linear Codes—0
Active Learning of Model Evidence Using Bayesian Quadrature—0
Active Learning of Mealy Machines with Timers—0
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning for affinity prediction of antibodies—0
Active Learning for Accurate Estimation of Linear Models—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
Minimum-Margin Active Learning—0
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Active learning of digenic functions with boolean matrix logic programming—0
Active learning of deep surrogates for PDEs: Application to metasurface design—0
Coupled reaction and diffusion governing interface evolution in solid-state batteries—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes—0
Active Learning of Convex Halfspaces on Graphs—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Active Learning of Continuous-time Bayesian Networks through Interventions—0
Active Learning of Classifiers with Label and Seed Queries—0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
Active Curriculum Learning—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active learning of causal probability trees—0
Active Learning Enables Extrapolation in Molecular Generative Models—0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
Active Learning of Abstract Plan Feasibility—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—0
Active Crowd Counting with Limited Supervision—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Active Learning Methods based on Statistical Leverage Scores—0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Active learning machine learns to create new quantum experiments—0
Active Covering—0
Active covariance estimation by random sub-sampling of variables—0
Active Learning in Video Tracking—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Active Learning Classification from a Signal Separation Perspective—0
MaxiMin Active Learning in Overparameterized Model Classes—0
Active learning in the geometric block model—0
Active Learning by Querying Informative and Representative Examples—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
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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