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 501–550 of 3073 papers

TitleStatusHype
Improve Cost Efficiency of Active Learning over Noisy Dataset—0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Prioritizing Informative Features and Examples for Deep Learning from Noisy DataCode0
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization—0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Batch Active Learning of Reward Functions from Human Preferences—0
Towards Efficient Active Learning in NLP via Pretrained Representations—0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data—0
Practice Makes Perfect: Planning to Learn Skill Parameter Policies—0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active LearningCode0
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information—0
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann EstimatorsCode1
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunitiesCode0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
Fairness Without Harm: An Influence-Guided Active Sampling ApproachCode0
Mode Estimation with Partial Feedback—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Bayesian Active Learning for Censored Regression—0
Key Patch Proposer: Key Patches Contain Rich InformationCode0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Active Preference Optimization for Sample Efficient RLHFCode0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling—0
Class-Balanced and Reinforced Active Learning on Graphs—0
Self-consistent Validation for Machine Learning Electronic Structure—0
Role-Playing Simulation Games using ChatGPT—0
Reinforcement Learning from Human Feedback with Active Queries—0
Transductive Active Learning: Theory and ApplicationsCode2
Active Few-Shot Fine-Tuning—0
Active Preference Learning for Large Language Models—0
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets—0
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learningCode2
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
ActiveDP: Bridging Active Learning and Data Programming—0
Direct Acquisition Optimization for Low-Budget Active Learning—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Information-Theoretic Active Correlation Clustering—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