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06/15/2023
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
High-content cellular imaging, transcriptomics, and proteomics data prov...
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05/31/2023
Noisy-label Learning with Sample Selection based on Noise Rate Estimate
Noisy-labels are challenging for deep learning due to the high capacity ...
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03/20/2023
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Noisy labels present a significant challenge in deep learning because mo...
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01/04/2023
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach
Learning from noisy labels plays an important role in the deep learning ...
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01/04/2023
Task Weighting in Meta-learning with Trajectory Optimisation
Developing meta-learning algorithms that are un-biased toward a subset o...
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09/02/2022
Instance-Dependent Noisy Label Learning via Graphical Modelling
Noisy labels are unavoidable yet troublesome in the ecosystem of deep le...
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08/10/2021
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Deep learning models achieve strong performance for radiology image clas...
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04/28/2021
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
We address the problem of modeling constrained hospital resources in the...
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01/27/2021
Similarity of Classification Tasks
Recent advances in meta-learning has led to remarkable performances on s...
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03/05/2020
PAC-Bayesian Meta-learning with Implicit Prior
We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-lea...
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07/27/2019
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
We introduce a new, rigorously-formulated Bayesian meta-learning algorit...
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07/17/2019
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Meta-training has been empirically demonstrated to be the most effective...
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01/23/2018
