Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

1 March 2017

Papers citing "Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank"

9 / 9 papers shown

Title
Block Circulant Adapter for Large Language Models Xinyu Ding Meiqi Wang Siyu Liao Zhongfeng Wang 31 0 0 01 May 2025
A Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compression Shupeng Ning Hanqing Zhu Chenghao Feng Jiaqi Gu David Z. Pan Ray T. Chen 36 0 0 01 Feb 2025
A Secure and Efficient Federated Learning Framework for NLP Jieren Deng Chenghong Wang Xianrui Meng Yijue Wang Ji Li Sheng Lin Shuo Han Fei Miao Sanguthevar Rajasekaran Caiwen Ding FedML 69 22 0 28 Jan 2022
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs Caiwen Ding Shuo Wang Ning Liu Kaidi Xu Yanzhi Wang Yun Liang MQ 13 89 0 29 Sep 2019
Understanding and Training Deep Diagonal Circulant Neural Networks Alexandre Araujo Benjamin Négrevergne Y. Chevaleyre Jamal Atif 19 4 0 29 Jan 2019
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs Zhe Li Caiwen Ding Siyue Wang Wujie Wen Youwei Zhuo ... Qinru Qiu Wenyao Xu X. Lin Xuehai Qian Yanzhi Wang MQ 9 64 0 12 Dec 2018
Learning Compressed Transforms with Low Displacement Rank Anna T. Thomas Albert Gu Tri Dao Atri Rudra Christopher Ré 22 40 0 04 Oct 2018
FFT-Based Deep Learning Deployment in Embedded Systems Sheng Lin Ning Liu M. Nazemi Hongjia Li Caiwen Ding Yanzhi Wang Massoud Pedram 33 52 0 13 Dec 2017
Benefits of depth in neural networks Matus Telgarsky 130 602 0 14 Feb 2016