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 2401–2450 of 3073 papers

TitleStatusHype
Active Learning for Skewed Data Sets—0
Active Learning for Sound Event Detection—0
Active Learning for Speech Recognition: the Power of Gradients—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Active Learning for Saddle Point Calculation—0
Active Learning for Undirected Graphical Model Selection—0
Active Learning for Video Classification with Frame Level Queries—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Active Learning for Vision-Language Models—0
Active Learning for Wireless IoT Intrusion Detection—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Active Learning from Crowd in Document Screening—0
Active Learning from Imperfect Labelers—0
Active Learning from Peers—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Active Learning from Weak and Strong Labelers—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
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
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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