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 851–900 of 3073 papers

TitleStatusHype
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis—0
Deep Active Learning with Structured Neural Depth Search—0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Active Learning on Medical Image—0
Agnostic Multi-Group Active Learning—0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification—0
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions—0
Scaling Evidence-based Instructional Design Expertise through Large Language Models—0
Learning the Pareto Front Using Bootstrapped Observation Samples—0
Let's Verify Step by StepCode4
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-informationCode1
atTRACTive: Semi-automatic white matter tract segmentation using active learningCode0
Parallelized Acquisition for Active Learning using Monte Carlo SamplingCode1
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
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
Active Learning for Natural Language Generation—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
Active Learning Principles for In-Context Learning with Large Language Models—0
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle—0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
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
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical ImagesCode2
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional SettingsCode0
Active Learning in Symbolic Regression with Physical Constraints—0
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
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
Machine-learning-accelerated simulations to enable automatic surface reconstructionCode1
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Accelerating Batch Active Learning Using Continual Learning Techniques—0
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Actively Discovering New Slots for Task-oriented ConversationCode0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks—0
Multi-Domain Learning From Insufficient Annotations—0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
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
Show:102550
← PrevPage 18 of 62Next →

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