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 2301–2350 of 3073 papers

TitleStatusHype
Learning in Unlabeled Networks - An Active Learning and Inference Approach—0
Learning Linear Utility Functions From Pairwise Comparison Queries—0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions—0
Learning Non-Markovian Reward Models in MDPs—0
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach—0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Learning Privately with Labeled and Unlabeled Examples—0
Learning Quantitative Automata Modulo Theories—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Learning switched systems from simulation models—0
Learning the Valuations of a k-demand Agent—0
Learning Time Dependent Choice—0
Learning to Actively Learn: A Robust Approach—0
Learning to Actively Learn Neural Machine Translation—0
Learning to Caption Images Through a Lifetime by Asking Questions—0
Learning to Detect Interesting Anomalies—0
Learning to Explore and Exploit in POMDPs—0
Learning to Learn for Few-shot Continual Active Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Learning to Make Decisions via Submodular Regularization—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
Learning to Sample: an Active Learning Framework—0
Learning User's confidence for active learning—0
Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination—0
Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging—0
Learning with a Drifting Target Concept—0
Learning with Labeling Induced Abstentions—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Least Probable Disagreement Region for Active Learning—0
Training Data Subset Search with Ensemble Active Learning—0
Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences—0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts—0
Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes—0
Leveraging Importance Weights in Subset Selection—0
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
Curriculum learning for data-driven modeling of dynamical systems—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Limitations of Active Learning With Deep Transformer Language Models—0
Limitations of Assessing Active Learning Performance at Runtime—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning—0
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning—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