no code implementations • 19 Oct 2023 • Haitian Jiang, Renjie Liu, Xiao Yan, Zhenkun Cai, Minjie Wang, David Wipf
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest.
1 code implementation • NeurIPS 2023 • Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
no code implementations • 24 Apr 2023 • Haitian Jiang, Dongliang Xiong, Xiaowen Jiang, Li Ding, Liang Chen, Kai Huang
In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach.
no code implementations • 18 Jan 2023 • Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
no code implementations • ICCV 2023 • Liyuan Ma, Tingwei Gao, Haitian Jiang, Haibin Shen, Kejie Huang
To leverage the advantages of both attention and flow simultaneously, we propose Wavelet-aware Image-based Pose Transfer (WaveIPT) to fuse the attention and flow in the wavelet domain.
no code implementations • 23 Jul 2022 • Haitian Jiang, Dongliang Xiong, Xiaowen Jiang, Aiguo Yin, Li Ding, Kai Huang
Deep neural networks have recently succeeded in digital halftoning using vanilla convolutional layers with high parallelism.