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
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
Learning Non-Markovian Reward Models in MDPs—0
Active Learning for Entity AlignmentCode0
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience—0
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics—0
Projection based Active Gaussian Process Regression for Pareto Front Modeling—0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning—0
Active and Incremental Learning with Weak Supervision—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—0
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition—0
K-NN active learning under local smoothness assumption—0
Noise-tolerant, Reliable Active Classification with Comparison Queries—0
Payoff Information and Learning in Signaling Games—0
Unsupervised Pool-Based Active Learning for Linear RegressionCode0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
Domain-independent Extraction of Scientific Concepts from Research ArticlesCode0
What is the Value of Data? On Mathematical Methods for Data Quality Estimation—0
Fair Active LearningCode0
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction—0
Learning the Valuations of a k-demand Agent—0
Active Learning in Video Tracking—0
Active Learning for Segmentation Based on Bayesian Sample Queries—0
Adversarial Representation Active LearningCode0
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis—0
MedCAT -- Medical Concept Annotation Tool—0
When Your Robot Breaks: Active Learning During Plant Failure—0
Incorporating Unlabeled Data into Distributionally Robust Learning—0
Disentanglement based Active LearningCode0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Parting with Illusions about Deep Active Learning—0
Measuring Mother-Infant Emotions By Audio Sensing—0
Large deviations for the perceptron model and consequences for active learning—0
A quantum active learning algorithm for sampling against adversarial attacks—0
Continual egocentric object recognitionCode0
Towards countering hate speech against journalists on social media—0
Active Learning of SVDD Hyperparameter Values—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
Cost Effective Active SearchCode0
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning—0
Deep imitation learning for molecular inverse problems—0
Sample Efficient Active Learning of Causal Trees—0
Merging Weak and Active Supervision for Semantic ParsingCode0
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach—0
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
A User Study of Perceived Carbon Footprint—0
Actively Learning Gaussian Process DynamicsCode0
Active Learning for Deep Detection Neural NetworksCode0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—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