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12/23/2022
Principled and Efficient Transfer Learning of Deep Models via Neural Collapse
With the ever-growing model size and the limited availability of labeled...
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12/02/2022
Avoiding spurious correlations via logit correction
Empirical studies suggest that machine learning models trained with empi...
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10/11/2022
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
Unsupervised denoising is a crucial challenge in real-world imaging appl...
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12/21/2021
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
Stroke rehabilitation seeks to increase neuroplasticity through the repe...
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11/03/2021
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
Automatic action identification from video and kinematic data is an impo...
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10/07/2021
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
Deep learning in the presence of noisy annotations has been studied exte...
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09/22/2021
Cramér-Rao bound-informed training of neural networks for quantitative MRI
Neural networks are increasingly used to estimate parameters in quantita...
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07/27/2021
Adaptive Denoising via GainTuning
Deep convolutional neural networks (CNNs) for image denoising are usuall...
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06/13/2021
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
In the last few years, deep learning classifiers have shown promising re...
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03/01/2021
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Normalization techniques have become a basic component in modern convolu...
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11/30/2020
Unsupervised Deep Video Denoising
Deep convolutional neural networks (CNNs) currently achieve state-of-the...
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10/24/2020
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
Denoising is a fundamental challenge in scientific imaging. Deep convolu...
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08/04/2020
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
During the COVID-19 pandemic, rapid and accurate triage of patients at t...
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06/30/2020
Early-Learning Regularization Prevents Memorization of Noisy Labels
We propose a novel framework to perform classification via deep learning...
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04/14/2020
Towards data-driven stroke rehabilitation via wearable sensors and deep learning
Recovery after stroke is often incomplete, but rehabilitation training m...
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03/30/2020
A Sampling Theorem for Deconvolution in Two Dimensions
This work studies the problem of estimating a two-dimensional superposit...
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02/10/2020
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Extraneous variables are variables that are irrelevant for a certain tas...
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11/09/2019
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Early detection is a crucial goal in the study of Alzheimer's Disease (A...
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06/13/2019
Robust and interpretable blind image denoising via bias-free convolutional neural networks
Deep convolutional networks often append additive constant ("bias") term...
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06/03/2019
Data-driven Estimation of Sinusoid Frequencies
Frequency estimation is a fundamental problem in signal processing, with...
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05/12/2019
Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems
Extracting information from nonlinear measurements is a fundamental chal...
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04/09/2019
Time-Series Analysis via Low-Rank Matrix Factorization: Applied to Infant-Sleep Data
We propose a nonparametric model for time series with missing data based...
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11/14/2018
A Learning-Based Framework for Line-Spectra Super-resolution
We propose a learning-based approach for estimating the spectrum of a mu...
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02/28/2018