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 1701–1750 of 3073 papers

TitleStatusHype
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool—0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities—0
Visual Causal Feature Learning—0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis—0
Visual Supervision in Bootstrapped Information Extraction—0
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas—0
VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)—0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning—0
Weakly Supervised Active Learning with Cluster Annotation—0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification—0
Weighted Ensembles for Active Learning with Adaptivity—0
Physics-informed active learning with simultaneous weak-form latent space dynamics identification—0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper—0
What can be learned from satisfaction assessments?—0
What does the free energy principle tell us about the brain?—0
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization—0
What is the Value of Data? On Mathematical Methods for Data Quality Estimation—0
What I've learned about annotating informal text (and why you shouldn't take my word for it)—0
What Makes Good Few-shot Examples for Vision-Language Models?—0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm—0
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?—0
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development—0
Black-box Generalization of Machine Teaching—0
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning—0
When does Active Learning Work?—0
When Your Robot Breaks: Active Learning During Plant Failure—0
Whom to Test? Active Sampling Strategies for Managing COVID-19—0
Wireless for Machine Learning—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Work Smart - Reducing Effort in Short-Answer Grading—0
Worst-Case Adaptive Submodular Cover—0
Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge—0
Zero-Round Active Learning—0
Zero-shot Active Learning Using Self Supervised Learning—0
Extended Active Learning Method—0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Coresets for Classification -- Simplified and Strengthened—0
Coresets for Classification – Simplified and Strengthened—0
Correlation-aware active learning for surgery video segmentation—0
Corruption Robust Active Learning—0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration—0
Cost-Based Budget Active Learning for Deep Learning—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-effective Variational Active Entity Resolution—0
Cost-efficient segmentation of electron microscopy images using active learning—0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—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