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 1601–1650 of 3073 papers

TitleStatusHype
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Active Learning of Convex Halfspaces on Graphs—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Online Active Learning with Surrogate Loss Functions—0
Learning with Labeling Induced Abstentions—0
Improving traffic sign recognition by active searchCode0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation—0
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Single Image Object Counting and Localizing using Active-Learning—0
Active Dialogue Simulation in Conversational Systems—0
Towards Computationally Feasible Deep Active Learning—0
Reducing the Long Tail Losses in Scientific Emulations with Active LearningCode0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Solving Multi-Arm Bandit Using a Few Bits of Communication—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Physics-enhanced deep surrogates for partial differential equations—0
An Interactive Visualization Tool for Understanding Active LearningCode0
Deep Unsupervised Active Learning on Learnable Graphs—0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Contextual Bayesian optimization with binary outputs—0
Active Learning for Rumor Identification on Social Media—0
Partial-Adaptive Submodular Maximization—0
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
Convergence of Uncertainty Sampling for Active Learning—0
Teaching an Active Learner with Contrastive Examples—0
RIM: Reliable Influence-based Active Learning on GraphsCode0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Failure-averse Active Learning for Physics-constrained Systems—0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Single-Modal Entropy based Active Learning for Visual Question Answering—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
A-Optimal Active LearningCode0
Active Learning for Deep Visual Tracking—0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning—0
Nuances in Margin Conditions Determine Gains in Active Learning—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Deep Active Learning by Leveraging Training Dynamics—0
Knowledge-driven Active LearningCode0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection—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