127 citations · 461 across the 65 of their papers we have counts for
14 papers · 1 filter
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Adam Hare, Yu Chen, Yinan Liu +2
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning…
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Junghoon Kim, Seyyedali Hosseinalipour, Taejoon Kim +2
Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single…
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Rajeev Sahay, Christopher G. Brinton, David J. Love
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.…
Can we Generalize and Distribute Private Representation Learning?
Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour +3
We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attrib…
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton +2
The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user fle…
Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G. Brinton, Nicolò Michelusi
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an im…
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
Qiong Wu, Adam Hare, Sirui Wang +4
Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are avail…
Fast-Convergent Federated Learning
Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour +3
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lowe…
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Junghoon Kim, Taejoon Kim, Morteza Hashemi +2
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the r…
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton +4
Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate awa…
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
Seyyedali Hosseinalipour, Christopher G. Brinton, Vaneet Aggarwal +2
Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the network ed…
Network-Aware Optimization of Distributed Learning for Fog Computing
Su Wang, Yichen Ruan, Yuwei Tu +3
Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this…
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
Junghoon Kim, Taejoon Kim, Morteza Hashemi +2
In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigura…
A Deep Learning Approach to Behavior-Based Learner Modeling
Yuwei Tu, Weiyu Chen, Christopher G. Brinton
The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper…