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07/31/2023
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
In real-world applications, perfect labels are rarely available, making ...
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07/29/2023
A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification
Immune repertoire classification, a typical multiple instance learning (...
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06/03/2023
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for ou...
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11/16/2022
Learning with Noisy Labels over Imbalanced Subpopulations
Learning with Noisy Labels (LNL) has attracted significant attention fro...
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10/06/2022
Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting
As an important part of intelligent transportation systems, traffic fore...
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06/15/2022
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
Detecting out-of-distribution (OOD) samples is crucial to the safe deplo...
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12/06/2021
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
Noisy labels damage the performance of deep networks. For robust learnin...
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09/09/2021
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
Conventional unsupervised domain adaptation (UDA) methods need to access...
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07/10/2021
