b'Jianyu Wang'

research

∙ 05/30/2023

FedDisco: Federated Learning with Discrepancy-Aware Collaboration

This work considers the category distribution heterogeneity in federated...

0 Rui Ye, et al. ∙

research

∙ 11/08/2022

Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance ...

0 Jianyu Wang, et al. ∙

research

∙ 10/14/2022

Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning

An oft-cited challenge of federated learning is the presence of heteroge...

0 John Nguyen, et al. ∙

research

∙ 10/14/2022

FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning

One of the key challenges in federated learning (FL) is local data distr...

0 Rui Ye, et al. ∙

research

∙ 06/01/2022

Federated Learning under Distributed Concept Drift

Federated Learning (FL) under distributed concept drift is a largely une...

0 Ellango Jothimurugesan, et al. ∙

research

∙ 05/25/2022

RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning

Prompting has shown impressive success in enabling large pretrained lang...

9 Mingkai Deng, et al. ∙

research

∙ 01/28/2022

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients

In classical federated learning, the clients contribute to the overall t...

0 Jianyu Wang, et al. ∙

research

∙ 09/16/2021

Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer

Personalized federated learning (FL) aims to train model(s) that can per...

1 Yae Jee Cho, et al. ∙

research

∙ 07/10/2021

DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering

Video question answering is a challenging task, which requires agents to...

0 Jianyu Wang, et al. ∙

research

∙ 06/04/2021

Local Adaptivity in Federated Learning: Convergence and Consistency

The federated learning (FL) framework trains a machine learning model us...

0 Jianyu Wang, et al. ∙

research

∙ 03/19/2021

BEHAVE: Behavior-Aware, Intelligent and Fair Resource Management for Heterogeneous Edge-IoT Systems

Edge computing is an emerging solution to support the future Internet of...

0 Ismail Alqerm, et al. ∙

research

∙ 02/24/2021

Deep NMF Topic Modeling

Nonnegative matrix factorization (NMF) based topic modeling methods do n...

0 Jianyu Wang, et al. ∙

research

∙ 02/06/2021

ORCA: Enabling an Owner-centric and Data-driven Management Paradigm for Future Heterogeneous Edge-IoT Systems

Integrating Internet of Things (IoT) and edge computing for "Edge-IoT" s...

0 Jianli Pan, et al. ∙

research

∙ 01/16/2021

Minimum-volume Multichannel Nonnegative matrix factorization for blind source separation

Multichannel blind source separation aims to recover the latent sources ...

0 Jianyu Wang, et al. ∙

research

∙ 10/03/2020

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Federated learning is a distributed optimization paradigm that enables a...

0 Yae Jee Cho, et al. ∙

research

∙ 07/15/2020

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

In federated optimization, heterogeneity in the clients' local datasets ...

0 Jianyu Wang, et al. ∙

research

∙ 06/19/2020

Band-limited Soft Actor Critic Model

Soft Actor Critic (SAC) algorithms show remarkable performance in comple...

0 Miguel Campo, et al. ∙

research

∙ 03/23/2020

Slow and Stale Gradients Can Win the Race

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous ...

1 Sanghamitra Dutta, et al. ∙

research

∙ 03/12/2020

Machine Learning on Volatile Instances

Due to the massive size of the neural network models and training datase...

8 Xiaoxi Zhang, et al. ∙

research

∙ 02/21/2020

Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD

Distributed stochastic gradient descent (SGD) is essential for scaling t...

0 Jianyu Wang, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 11/22/2019

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Neural networks are known to be vulnerable to carefully crafted adversar...

21 Yantao Lu, et al. ∙

research

∙ 10/24/2019

Deep topic modeling by multilayer bootstrap network and lasso

Topic modeling is widely studied for the dimension reduction and analysi...

0 Jianyu Wang, et al. ∙

research

∙ 10/07/2019

Compositional Generalization for Primitive Substitutions

Compositional generalization is a basic mechanism in human language lear...

0 Yuanpeng Li, et al. ∙

research

∙ 10/01/2019

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Distributed optimization is essential for training large models on large...

0 Jianyu Wang, et al. ∙

research

∙ 07/24/2019

Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training

We introduce a feature scattering-based adversarial training approach fo...

0 Haichao Zhang, et al. ∙

research

∙ 07/24/2019

Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks

Conventional adversarial training methods using attacks that manipulate ...

0 Haichao Zhang, et al. ∙

research

∙ 07/24/2019

Towards Adversarially Robust Object Detection

Object detection is an important vision task and has emerged as an indis...

9 Haichao Zhang, et al. ∙

research

∙ 05/23/2019

MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling

The trade-off between convergence error and communication delays in dece...

0 Jianyu Wang, et al. ∙

research

∙ 11/26/2018

Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks

In this paper, we study fast training of adversarially robust models. Fr...

0 Jianyu Wang, et al. ∙

research

∙ 11/02/2018

Zero-Shot Transfer VQA Dataset

Acquiring a large vocabulary is an important aspect of human intelligenc...

0 Yuanpeng Li, et al. ∙

research

∙ 10/19/2018

Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD

Large-scale machine learning training, in particular distributed stochas...

0 Jianyu Wang, et al. ∙

research

∙ 08/22/2018

Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms

State-of-the-art distributed machine learning suffers from significant d...

0 Jianyu Wang, et al. ∙

research

∙ 06/16/2018

Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives

Mobile devices supporting the "Internet of Things" (IoT), often have lim...

0 Jianyu Wang, et al. ∙

research

∙ 06/16/2018

Key Enabling Technologies for Secure and Scalable Future Fog-IoT Architecture: A Survey

Fog or Edge computing has recently attracted broad attention from both i...

0 Jianli Pan, et al. ∙

research

∙ 06/16/2018

EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts

The emerging Internet of Things (IoT) is facing significant scalability ...

0 Jianli Pan, et al. ∙

research

∙ 03/31/2018

Adversarial Attacks and Defences Competition

To accelerate research on adversarial examples and robustness of machine...

0 Alexey Kurakin, et al. ∙

research

∙ 03/19/2018

Improving Transferability of Adversarial Examples with Input Diversity

Though convolutional neural networks have achieved state-of-the-art perf...

0 Cihang Xie, et al. ∙

research

∙ 11/13/2017

Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory Mum...

0 Jianyu Wang, et al. ∙

research

∙ 11/06/2017

Mitigating adversarial effects through randomization

Convolutional neural networks have demonstrated their powerful ability o...

0 Cihang Xie, et al. ∙

research

∙ 09/14/2017

DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion

In this paper, we study the task of detecting semantic parts of an objec...

0 Zhishuai Zhang, et al. ∙

research

∙ 07/25/2017

Detecting Semantic Parts on Partially Occluded Objects

In this paper, we address the task of detecting semantic parts on partia...

0 Jianyu Wang, et al. ∙

research

∙ 03/24/2017

Adversarial Examples for Semantic Segmentation and Object Detection

It has been well demonstrated that adversarial examples, i.e., natural i...

0 Cihang Xie, et al. ∙

research

∙ 12/18/2014

Semantic Part Segmentation using Compositional Model combining Shape and Appearance

In this paper, we study the problem of semantic part segmentation for an...

0 Jianyu Wang, et al. ∙

Jianyu Wang

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