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 2601–2650 of 3073 papers

TitleStatusHype
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not—0
Addressing Limited Data for Textual Entailment Across Domains—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
AdjointNet: Constraining machine learning models with physics-based codes—0
A domain-decomposed VAE method for Bayesian inverse problems—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Adversarial Sampling for Active Learning—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
Adversarial Vulnerability of Active Transfer Learning—0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
A Finite-Horizon Approach to Active Level Set Estimation—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Deep Active Learning for Anomaly Detection—0
Active learning for structural reliability: survey, general framework and benchmark—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Agnostic Active Learning Without Constraints—0
Agnostic Multi-Group Active Learning—0
A Graph-Based Approach for Active Learning in Regression—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
AI For Fraud Awareness—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—0
A Lagrangian Duality Approach to Active Learning—0
ALANNO: An Active Learning Annotation System for Mortals—0
ALARM: Active LeArning of Rowhammer Mitigations—0
ALdataset: a benchmark for pool-based active learning—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Show:102550
← PrevPage 53 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