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 1601–1650 of 3073 papers

TitleStatusHype
On Deep Unsupervised Active Learning—0
One-Bit Active Query With Contrastive Pairs—0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond—0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
One-Round Active Learning—0
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models—0
One Size Does Not Fit All: The Case for Personalised Word Complexity Models—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Online Active Learning for Cost Sensitive Domain Adaptation—0
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders—0
Online Active Learning For Sound Event Detection—0
Disagreement-based Active Learning in Online Settings—0
Online Active Learning of Reject Option Classifiers—0
Online Active Learning with Surrogate Loss Functions—0
Online Active Linear Regression via Thresholding—0
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems—0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation—0
Online Bandit Learning with Offline Preference Data for Improved RLHF—0
Online Body Schema Adaptation through Cost-Sensitive Active Learning—0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision—0
Online Learning of Non-Markovian Reward Models—0
Learning Novel Objects Continually Through Curiosity—0
Online Submodular Set Cover, Ranking, and Repeated Active Learning—0
Online Tool Condition Monitoring Based on Parsimonious Ensemble+—0
On Locality in Graph Learning via Graph Neural Network—0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL—0
On risk-based active learning for structural health monitoring—0
On robust risk-based active-learning algorithms for enhanced decision support—0
On State Variables, Bandit Problems and POMDPs—0
On Statistical Bias In Active Learning: How and When To Fix It—0
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search—0
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning—0
Active learning of effective Hamiltonian for super-large-scale atomic structures—0
On the Geometry of Deep Bayesian Active Learning—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning—0
On the Limitations of Simulating Active Learning—0
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?—0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise—0
On the Pros and Cons of Active Learning for Moral Preference Elicitation—0
On the Query Strategies for Efficient Online Active Distillation—0
On the Robustness of Active Learning—0
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow—0
On weighted uncertainty sampling in active learning—0
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis—0
Offline Preference-Based Apprenticeship Learning—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