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 1476–1500 of 3073 papers

TitleStatusHype
Online Active Learning with Surrogate Loss Functions—0
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual InformationCode1
DeepAL: Deep Active Learning in PythonCode1
Improving traffic sign recognition by active searchCode0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Learning at the ImageNet ScaleCode1
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic SegmentationCode1
Active Learning Meets Optimized Item SelectionCode1
Fink: early supernovae Ia classification using active learningCode1
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation—0
YMIR: A Rapid Data-centric Development Platform for Vision ApplicationsCode1
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification—0
GFlowNet FoundationsCode1
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Towards Computationally Feasible Deep Active Learning—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Active Dialogue Simulation in Conversational Systems—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Single Image Object Counting and Localizing using Active-Learning—0
Code-free development and deployment of deep segmentation models for digital pathologyCode1
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
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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