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 2201–2250 of 3073 papers

TitleStatusHype
A Competitive Algorithm for Agnostic Active Learning—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
A Survey on Curriculum Learning—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
A critical look at the current train/test split in machine learning—0
ActDroid: An active learning framework for Android malware detection—0
Action State Update Approach to Dialogue Management—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Adversarial Domain Adaptation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Active and Adaptive Sequential learning—0
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes—0
Active and Dynamic Beam Tracking UnderStochastic Mobility—0
Active and Incremental Learning with Weak Supervision—0
Active and passive learning of linear separators under log-concave distributions—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—0
Active and sparse methods in smoothed model checking—0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration—0
Active anomaly detection based on deep one-class classification—0
Active Anomaly Detection for time-domain discoveries—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
Bucketized Active Sampling for Learning ACOPF—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Active classification with comparison queries—0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
Active Community Detection with Maximal Expected Model Change—0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Active covariance estimation by random sub-sampling of variables—0
Active Covering—0
Active Crowd Counting with Limited Supervision—0
Active Curriculum Learning—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
Active Deep Decoding of Linear Codes—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls—0
Active Deep Learning for Classification of Hyperspectral Images—0
Active Learning Guided by Efficient Surrogate Learners—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Active Dialogue Simulation in Conversational Systems—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
Active Discriminative Text Representation 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