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 1101–1125 of 3073 papers

TitleStatusHype
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias—0
UMat: Uncertainty-Aware Single Image High Resolution Material Capture—0
Active Learning for Natural Language Generation—0
Active Learning Principles for In-Context Learning with Large Language Models—0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning—0
On the Limitations of Simulating Active Learning—0
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional SettingsCode0
Active Learning in Symbolic Regression with Physical Constraints—0
On Dataset Transferability in Active Learning for TransformersCode0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Accelerating Batch Active Learning Using Continual Learning Techniques—0
Actively Discovering New Slots for Task-oriented ConversationCode0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Multi-Domain Learning From Insufficient Annotations—0
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks—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