research
∙
04/03/2023
X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
Structured, or tabular, data is the most common format in data science. ...
share
research
∙
10/23/2022
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Although Physics-Informed Neural Networks (PINNs) have been successfully...
research
∙
07/25/2022
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction
Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolu...
research
∙
02/25/2022
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
The adversarial robustness of a neural network mainly relies on two fact...
research
∙
02/13/2022
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Deep image inpainting research mainly focuses on constructing various ne...
research
∙
07/31/2021
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
Despite tremendous progress in missing data imputation task, designing n...
research
∙
01/27/2021
Change point detection and image segmentation for time series of astrophysical images
Many astrophysical phenomena are time-varying, in the sense that their i...
research
∙
12/24/2020
Adversarial Momentum-Contrastive Pre-Training
Deep neural networks are vulnerable to semantic invariant corruptions an...
research
∙
10/08/2020
Improve Adversarial Robustness via Weight Penalization on Classification Layer
It is well-known that deep neural networks are vulnerable to adversarial...
research
∙
12/03/2019
A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
Deflation method is an iterative technique that searches the sparse load...
research
∙
11/30/2018
The Graph-based Broad Behavior-Aware Recommendation System for Interactive News
In this paper, we propose a heuristic recommendation system for interact...
research
∙
05/21/2018
SmoothOut: Smoothing Out Sharp Minima for Generalization in Large-Batch Deep Learning
In distributed deep learning, a large batch size in Stochastic Gradient ...
research
∙
05/22/2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
High network communication cost for synchronizing gradients and paramete...
research
∙
03/28/2017
