Search Results for author: Wei Shi

Found 33 papers, 8 papers with code

Efficient and Secure Federated Learning for Financial Applications

no code implementations15 Mar 2023 Tao Liu, Zhi Wang, Hui He, Liangliang Lin, Wei Shi, Ran An, Chenhao Li

Experiments show that under different Non-IID experiment settings, our method can reduce the upload communication cost to about 2. 9% to 18. 9% of the conventional federated learning algorithm when the sparse rate is 0. 01.

Federated Learning

Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation

no code implementations9 Feb 2023 Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

Unlike prior works which mostly ignore the impact of near-field beam-split (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB.

Federated Learning

PAMI: partition input and aggregate outputs for model interpretation

no code implementations7 Feb 2023 Wei Shi, Wentao Zhang, Weishi Zheng, Ruixuan Wang

There is an increasing demand for interpretation of model predictions especially in high-risk applications.

Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification

1 code implementation25 Aug 2022 Jianbing Wu, Hong Liu, Wei Shi, Hao Tang, Jingwen Guo

To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network.

Human Parsing Person Re-Identification

Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

no code implementations7 Aug 2022 Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks.

Federated Learning

RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL

no code implementations13 Jul 2022 Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Pan, Song Han, Nan Sun

To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.

Bayesian Optimization

Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation

no code implementations13 Jul 2022 Ahmet M. Elbir, Wei Shi, Kumar Vijay Mishra, Symeon Chatzinotas

This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon, i. e., beam misalignment because of frequency-independent analog beamformers, and computational complexity because of the usage of ultra-massive number of antennas to compensate propagation losses.

Federated Learning Multi-Task Learning

Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks

no code implementations24 Jun 2022 Ahmet M. Elbir, Wei Shi, Kumar Vijay Mishra, Anastasios K. Papazafeiropoulos, Symeon Chatzinotas

Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802. 11p vehicular channels.

BIG-bench Machine Learning

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

no code implementations Findings (ACL) 2022 Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei LI, Yanghua Xiao, Hao Zhou

Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).

Explanation Generation Question Answering

Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer

1 code implementation5 Dec 2021 Tao Wang, Hong Liu, Pinhao Song, Tianyu Guo, Wei Shi

Therefore, we propose a transformer-based Pose-guided Feature Disentangling (PFD) method by utilizing pose information to clearly disentangle semantic components (e. g. human body or joint parts) and selectively match non-occluded parts correspondingly.

Person Re-Identification

Semi-supervised t-SNE for Millimeter-wave Wireless Localization

no code implementations26 Nov 2021 Junquan Deng, Wei Shi, Jian Hu, Xianlong Jiao

We consider the mobile localization problem in future millimeter-wave wireless networks with distributed Base Stations (BSs) based on multi-antenna channel state information (CSI).

Preserving Dense Features for Ki67 Nuclei Detection

no code implementations10 Nov 2021 Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles, Wei Shi, Fei-Fei Liu, Susan Done, April Khademi

Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images.

Understanding of Kernels in CNN Models by Suppressing Irrelevant Visual Features in Images

1 code implementation25 Aug 2021 Jia-Xin Zhuang, Wanying Tao, Jianfei Xing, Wei Shi, Ruixuan Wang, Wei-Shi Zheng

In this paper, a simple yet effective optimization method is proposed to interpret the activation of any kernel of interest in CNN models.

Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain

no code implementations ACL 2021 Wei Shi, Vera Demberg

Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s).

Implicit Discourse Relation Classification

Joint Embedding in Named Entity Linking on Sentence Level

no code implementations12 Feb 2020 Wei Shi, Si-Yuan Zhang, Zhiwei Zhang, Hong Cheng, Jeffrey Xu Yu

The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.

Entity Linking Knowledge Graphs

A Hybrid Model for Globally Coherent Story Generation

no code implementations WS 2019 Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, Asad Sayeed

The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan.

Story Generation

Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification

no code implementations WS 2019 Wei Shi, Frances Yung, Vera Demberg

Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues.

Discourse Parsing General Classification +2

Accelerating Incremental Gradient Optimization with Curvature Information

1 code implementation31 May 2018 Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich, Anna Scaglione

This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.

Curvature-aided Incremental Aggregated Gradient Method

no code implementations24 Oct 2017 Hoi-To Wai, Wei Shi, Angelia Nedic, Anna Scaglione

We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.

A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates

no code implementations25 Apr 2017 Zhi Li, Wei Shi, Ming Yan

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms.

On the Need of Cross Validation for Discourse Relation Classification

no code implementations EACL 2017 Wei Shi, Vera Demberg

The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic.

Classification General Classification +4

Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes

no code implementations19 Sep 2016 Angelia Nedić, Alex Olshevsky, Wei Shi, César A. Uribe

A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.

Distributed Optimization

EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

no code implementations24 Apr 2014 Wei Shi, Qing Ling, Gang Wu, Wotao Yin

In this paper, we develop a decentralized algorithm for the consensus optimization problem $$\min\limits_{x\in\mathbb{R}^p}~\bar{f}(x)=\frac{1}{n}\sum\limits_{i=1}^n f_i(x),$$ which is defined over a connected network of $n$ agents, where each function $f_i$ is held privately by agent $i$ and encodes the agent's data and objective.

Optimization and Control

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