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 2001–2050 of 3073 papers

TitleStatusHype
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
PAL : Pretext-based Active Learning—0
Learning to Actively Learn: A Robust Approach—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
What can be learned from satisfaction assessments?—0
A Survey on Curriculum Learning—0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
Pool-based sequential active learning with multi kernels—0
Robot Design With Neural Networks, MILP Solvers and Active Learning—0
Exploiting Context for Robustness to Label Noise in Active Learning—0
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
ALdataset: a benchmark for pool-based active learning—0
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming—0
Robust Active Learning Strategies for Model Variability—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
Identifying Wrongly Predicted Samples: A Method for Active Learning—0
Meta-Active Learning for Node Response Prediction in Graphs—0
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transientsCode0
Pre-trained Language Model Based Active Learning for Sentence Matching—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
Zero-shot Active Learning with Topological Clustering for Multiclass Classification—0
Model Exploration with Cost-Aware Learning—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps—0
Data-efficient Online Classification with Siamese Networks and Active Learning—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning—0
Online Learning of Non-Markovian Reward Models—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Mean-Variance Analysis in Bayesian Optimization under Uncertainty—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks—0
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Wireless for Machine Learning—0
A Survey of Deep Active LearningCode0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—0
Show:102550
← PrevPage 41 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