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 2701–2750 of 3073 papers

TitleStatusHype
An Approach to Reducing Annotation Costs for BioNLP—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model—0
An Efficient Active Learning Framework for New Relation Types—0
An Efficient Active Learning Pipeline for Legal Text Classification—0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
A New Vision of Collaborative Active Learning—0
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
An Eye-tracking Study of Named Entity Annotation—0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting—0
An information-matching approach to optimal experimental design and active learning—0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance—0
Annotating named entities in clinical text by combining pre-annotation and active learning—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage—0
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks—0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning—0
基於多模態主動式學習法進行需備標記樣本之挑選用於候用校長評鑑之自動化評分系統建置(A Multimodal Active Learning Approach toward Identifying Samples to Label during the Development of Automatic Oral Presentation Assessment System for Pre-service Principals Certification Program)[In Chinese]—0
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Applied metamodelling for ATM performance simulations—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
A Pre-trained Data Deduplication Model based on Active Learning—0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
A quantum active learning algorithm for sampling against adversarial attacks—0
Are All Training Examples Created Equal? An Empirical Study—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—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