Peng Jiang

ML-CGAN: Conditional Generative Adversarial Network with a Meta-learner Structure for High-Quality Image Generation with Few Training Data

G. Zhong, Rui Zhang, Yanan Wang, 2021, Cognitive Computation.

ML-CGAN: Conditional Generative Adversarial Network with a Meta-learner Structure for High-Quality Image Generation with Few Training Data

Guoqiang Zhong, Ying Ma, Wen Liu, 2021, Cognitive Computation.

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training

Shihui Song, Peng Jiang, Lihan Hu, 2022, NeurIPS.

Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning

Yanzhi Wang, Peng Jiang, Xiaolong Ma, 2020, PACT.

Rethinking graph data placement for graph neural network training on multiple GPUs

Shihui Song, Peng Jiang, 2022, ICS.

Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks pdf

Peng Jiang, Masuma Akter Rumi, Peng Jiang, 2021, ArXiv.

Rethinking graph data placement for graph neural network training on multiple GPUs

Shihui Song, Peng Jiang, 2022, PPoPP.

Latent Embedded Graphs for Image and Shape Interpolation

Vinayak R. Krishnamurthy, Ronak R. Mohanty, Peng Jiang, 2021, Comput. Aided Des..