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 2426–2450 of 3073 papers

TitleStatusHype
Active Learning in Gaussian Process State Space Model—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
Active Learning in Physics: From 101, to Progress, and Perspective—0
Active Learning in Recommendation Systems with Multi-level User Preferences—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Active Learning in Symbolic Regression with Physical Constraints—0
Active learning in the geometric block model—0
MaxiMin Active Learning in Overparameterized Model Classes—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Active Learning in Video Tracking—0
Active learning machine learns to create new quantum experiments—0
Active Learning Methods based on Statistical Leverage Scores—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Active Learning of Abstract Plan Feasibility—0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
Active learning of causal probability trees—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active Learning of Classifiers with Label and Seed Queries—0
Active Learning of Continuous-time Bayesian Networks through Interventions—0
Active Learning of Convex Halfspaces on Graphs—0
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes—0
Active learning of deep surrogates for PDEs: Application to metasurface design—0
Active learning of digenic functions with boolean matrix logic programming—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Show:102550
← PrevPage 98 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