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 1151–1200 of 3073 papers

TitleStatusHype
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors—0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
Active learning using weakly supervised signals for quality inspection—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
Active Learning for Coreference Resolution—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active learning using region-based sampling—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—0
Information Losses in Neural Classifiers from Sampling—0
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis—0
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Active learning using adaptable task-based prioritisation—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
Active Learning for Coreference Resolution—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Active learning for data streams: a survey—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Active Learning under Label Shift—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis—0
Active learning to optimise time-expensive algorithm selection—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey of Latent Factor Models in Recommender Systems—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A Survey of Active Learning for Natural Language Processing—0
Active Learning for Contextual Search with Binary Feedbacks—0
A survey of active learning algorithms for supervised remote sensing image classification—0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
Active Learning Solution on Distributed Edge Computing—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Active Dialogue Simulation in Conversational Systems—0
A Structured Perspective of Volumes on Active Learning—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
Active Learning: Problem Settings and Recent Developments—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—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