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 2501–2550 of 3073 papers

TitleStatusHype
Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner—0
Batch Active Preference-Based Learning of Reward FunctionsCode0
Discovering General-Purpose Active Learning StrategiesCode0
Active Learning for New Domains in Natural Language Understanding—0
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared taskCode1
Learning to Actively Learn Neural Machine Translation—0
Using active learning to expand training data for implicit discourse relation recognitionCode0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Visual Supervision in Bootstrapped Information Extraction—0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice—0
Target-Independent Active Learning via Distribution-Splitting—0
Generative Adversarial Active Learning for Unsupervised Outlier DetectionCode0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
Active Learning for Deep Object Detection—0
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis—0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and SummarizationCode0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems—0
Active Anomaly Detection via EnsemblesCode1
Events Beyond ACE: Curated Training for Events—0
Query-Efficient Black-Box Attack by Active Learning—0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models—0
Learning Time Dependent Choice—0
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection—0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization—0
Learning a Policy for Opportunistic Active Learning—0
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement LearningCode0
Adversarial Sampling for Active Learning—0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study—0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Active Learning for Regression Using Greedy SamplingCode0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
Active Learning for Wireless IoT Intrusion Detection—0
Active DOP: A constituency treebank annotation tool with online learningCode0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive AnnotationCode0
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Sprucing up the trees -- Error detection in treebanks—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
A Structured Perspective of Volumes on Active Learning—0
Noise Contrastive Priors for Functional UncertaintyCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—0
Show:102550
← PrevPage 51 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