1 code implementation • 4 May 2023 • Sai Zhang, Yuwei Hu, Xiaojie Wang, Caixia Yuan
The errors from DST might misguide the dialog policy, and the system action brings extra difficulties for the DST module.
no code implementations • 25 Jul 2022 • Yuwei Hu, Jiajie Li, Zhongming Yu, Zhiru Zhang
To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane.
1 code implementation • Findings (ACL) 2022 • Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun, Caixia Yuan, Xiaojie Wang
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
Ranked #1 on SSTOD on SSD_NAME
no code implementations • 16 Sep 2021 • Mark Buckler, Neil Adit, Yuwei Hu, Zhiru Zhang, Adrian Sampson
Our key insights are that 1) pointwise convolutions commute with frequency transformation and thus can be computed in the frequency domain without modification, 2) each channel within a given layer has a different level of sensitivity to frequency domain pruning, and 3) each channel's sensitivity to frequency pruning is approximately monotonic with respect to frequency.
no code implementations • 26 Aug 2020 • Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang
FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge.
3 code implementations • 28 Jan 2019 • Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.
no code implementations • CVPR 2019 • Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i. e. ShuffleNet) and block-circulant networks (i. e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique.
1 code implementation • 12 Feb 2018 • Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.