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 1101–1150 of 3073 papers

TitleStatusHype
Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop—0
Batch Active Learning from the Perspective of Sparse Approximation—0
Entity Matching by Pool-based Active Learning—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Oracle-guided Contrastive Clustering—0
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models—0
Learning to Detect Interesting Anomalies—0
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
COMET-QE and Active Learning for Low-Resource Machine Translation—0
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active LearningCode0
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
Worst-Case Adaptive Submodular Cover—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Multi-Objective GFlowNetsCode1
Bayesian Optimization with Conformal Prediction SetsCode1
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
Targeted active learning for probabilistic modelsCode0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Learning Preferences for Interactive AutonomyCode0
A Survey of Active Learning for Natural Language Processing—0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities—0
ISEE.U: Distributed online active target localization with unpredictable targets—0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
Active Learning from the WebCode1
GFlowCausal: Generative Flow Networks for Causal DiscoveryCode0
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active LearningCode1
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain ShiftCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Geometric Active Learning for Segmentation of Large 3D Volumes—0
TiDAL: Learning Training Dynamics for Active LearningCode0
Efficient Bayesian Updates for Deep Learning via Laplace ApproximationsCode1
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose EstimationCode1
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks—0
Deep Active Ensemble Sampling For Image Classification—0
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes—0
Few-Shot Continual Active Learning by a Robot—0
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design—0
Is margin all you need? An extensive empirical study of active learning on tabular data—0
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer ModelsCode0
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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