306 citations · 388 across the 10 of their papers we have counts for
10 papers
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +4
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performa…
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Zhenyu Guo, Shuai Zheng, Zhizhe Liu +2
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but…
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +3
Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods…
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov, Radu Timofte, Zhilu Zhang +36
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to…
AIM 2020 Challenge on Rendering Realistic Bokeh
Andrey Ignatov, Radu Timofte, Ming Qian +32
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solvin…
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
Ming Qian, Congyu Qiao, Jiamin Lin +4
A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. Howeve…
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Shaohuai Shi, Xianhao Zhou, Shutao Song +21
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters. However, on public cloud clusters, due to the m…
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
Haidong Rong, Yangzihao Wang, Feihu Zhou +8
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduc…
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes
Xianyan Jia, Shutao Song, Wei He +11
Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes…
An Adaptive Descriptor Design for Object Recognition in the Wild
Zhenyu Guo, Z. Jane Wang
Digital images nowadays have various styles of appearance, in the aspects of color tones, contrast, vignetting, and etc. These 'picture styles' are directly related to the scene ra…