research
∙
05/30/2023
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
This work considers the category distribution heterogeneity in federated...
share
research
∙
11/08/2022
Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation
Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance ...
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...
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...
research
∙
06/01/2022
Federated Learning under Distributed Concept Drift
Federated Learning (FL) under distributed concept drift is a largely une...
research
∙
05/25/2022
RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning
Prompting has shown impressive success in enabling large pretrained lang...
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...
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...
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...
research
∙
06/04/2021
Local Adaptivity in Federated Learning: Convergence and Consistency
The federated learning (FL) framework trains a machine learning model us...
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...
research
∙
02/24/2021
Deep NMF Topic Modeling
Nonnegative matrix factorization (NMF) based topic modeling methods do n...
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...
research
∙
01/16/2021
Minimum-volume Multichannel Nonnegative matrix factorization for blind source separation
Multichannel blind source separation aims to recover the latent sources ...
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...
research
∙
07/15/2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
In federated optimization, heterogeneity in the clients' local datasets ...
research
∙
06/19/2020
Band-limited Soft Actor Critic Model
Soft Actor Critic (SAC) algorithms show remarkable performance in comple...
research
∙
03/23/2020
Slow and Stale Gradients Can Win the Race
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous ...
research
∙
03/12/2020
Machine Learning on Volatile Instances
Due to the massive size of the neural network models and training datase...
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...
research
∙
12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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...
research
∙
10/24/2019
Deep topic modeling by multilayer bootstrap network and lasso
Topic modeling is widely studied for the dimension reduction and analysi...
research
∙
10/07/2019
Compositional Generalization for Primitive Substitutions
Compositional generalization is a basic mechanism in human language lear...
research
∙
10/01/2019
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Distributed optimization is essential for training large models on large...
research
∙
07/24/2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
We introduce a feature scattering-based adversarial training approach fo...
research
∙
07/24/2019
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
Conventional adversarial training methods using attacks that manipulate ...
research
∙
07/24/2019
Towards Adversarially Robust Object Detection
Object detection is an important vision task and has emerged as an indis...
research
∙
05/23/2019
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
The trade-off between convergence error and communication delays in dece...
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...
research
∙
11/02/2018
Zero-Shot Transfer VQA Dataset
Acquiring a large vocabulary is an important aspect of human intelligenc...
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...
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...
research
∙
06/16/2018
Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives
Mobile devices supporting the "Internet of Things" (IoT), often have lim...
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...
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 ...
research
∙
03/31/2018
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine...
research
∙
03/19/2018
Improving Transferability of Adversarial Examples with Input Diversity
Though convolutional neural networks have achieved state-of-the-art perf...
research
∙
11/13/2017
Visual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory Mum...
research
∙
11/06/2017
Mitigating adversarial effects through randomization
Convolutional neural networks have demonstrated their powerful ability o...
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...
research
∙
07/25/2017
Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partia...
research
∙
03/24/2017
Adversarial Examples for Semantic Segmentation and Object Detection
It has been well demonstrated that adversarial examples, i.e., natural i...
research
∙
12/18/2014