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 1101–1150 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
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
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
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
NewsPanda: Media Monitoring for Timely Conservation ActionCode0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling—0
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next stepsCode0
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification—0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
OpenAL: Evaluation and Interpretation of Active Learning StrategiesCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Towards Active Learning for Action Spotting in Association Football Videos—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection—0
Fairness-Aware Data Valuation for 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