no code implementations • 19 Oct 2023 • Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao
These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence.
1 code implementation • 18 Jan 2022 • Cole Hawkins, Alec Koppel, Zheng Zhang
A fundamental challenge in Bayesian inference is efficient representation of a target distribution.
no code implementations • 2 Nov 2021 • Cole Hawkins, Haichuan Yang, Meng Li, Liangzhen Lai, Vikas Chandra
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices.
no code implementations • 12 Oct 2021 • Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina, George Karypis
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.
no code implementations • 11 May 2021 • Yao Chen, Cole Hawkins, Kaiqi Zhang, Zheng Zhang, Cong Hao
This paper emphasizes the importance and efficacy of training, quantization and accelerator design, and calls for more research breakthroughs in the area for AI on the edge.
1 code implementation • 17 Oct 2020 • Cole Hawkins, Xing Liu, Zheng Zhang
This paper presents a novel end-to-end framework for low-rank tensorized training of neural networks.
1 code implementation • 24 May 2019 • Cole Hawkins, Zheng Zhang
Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms.
no code implementations • 6 Sep 2018 • Cole Hawkins, Zheng Zhang
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis.