no code implementations • 18 May 2024 • ChenChen Liu, Wenjun Jiang, Xiaojun Yuan
In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system.
no code implementations • 29 Nov 2023 • Chenhui Xu, Fuxun Yu, Zirui Xu, ChenChen Liu, JinJun Xiong, Xiang Chen
Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features.
Hardware Aware Neural Architecture Search Neural Architecture Search
no code implementations • 28 Nov 2021 • Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, ChenChen Liu, Zhi Tian, Xiang Chen
Federated learning learns from scattered data by fusing collaborative models from local nodes.
no code implementations • 28 Nov 2021 • Fuxun Yu, Di Wang, Longfei Shangguan, Minjia Zhang, Xulong Tang, ChenChen Liu, Xiang Chen
With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS).
no code implementations • 15 Aug 2020 • Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, ChenChen Liu, Zhi Tian, Xiang Chen
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
no code implementations • 14 Aug 2020 • Fuxun Yu, ChenChen Liu, Di Wang, Yanzhi Wang, Xiang Chen
Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout.
no code implementations • CVPR 2020 • Chenchen Liu, Yang Jin, Kehan Xu, Guoqiang Gong, Yadong Mu
Different from relationships in static images, videos contain an addition temporal channel.
no code implementations • CVPR 2019 • Chenchen Liu, Xinyu Weng, Yadong Mu
To address this issue, this work proposes a novel framework that simultaneously solving two inherently related tasks - crowd counting and localization.
no code implementations • 10 May 2019 • Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks.
no code implementations • ICLR 2019 • Zhuwei Qin, Fuxun Yu, ChenChen Liu, Xiang Chen
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
no code implementations • NIPS Workshop CDNNRIA 2018 • Zhuwei Qin, Fuxun Yu, ChenChen Liu, Xiang Chen
We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality.
no code implementations • ICLR 2019 • Zhuwei Qin, Fuxun Yu, ChenChen Liu, Xiang Chen
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
no code implementations • ICLR 2019 • Fuxun Yu, ChenChen Liu, Yanzhi Wang, Liang Zhao, Xiang Chen
One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization.
no code implementations • 4 Sep 2018 • Zirui Xu, Fuxun Yu, ChenChen Liu, Xiang Chen
In this work, we propose HASP -- a high-performance security enhancement approach to solve this security issue on mobile devices.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 23 May 2018 • Fuxun Yu, Zirui Xu, Yanzhi Wang, ChenChen Liu, Xiang Chen
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications. However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures.
1 code implementation • 30 Apr 2018 • Zhuwei Qin, Fuxun Yu, ChenChen Liu, Xiang Chen
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc.