55 citations · 185 across the 12 of their papers we have counts for
12 papers
Deep Complex Networks for Protocol-Agnostic Radio Frequency Device Fingerprinting in the Wild
Ioannis Agadakos, Nikolaos Agadakos, Jason Polakis +1
Researchers have demonstrated various techniques for fingerprinting and identifying devices. Previous approaches have identified devices from their network traffic or transmitted s…
Image Classification with Hierarchical Multigraph Networks
Boris Knyazev, Xiao Lin, Mohamed R. Amer +1
Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data. Despite being general, GCNs are admittedly inferior to convolutional ne…
Data-Efficient Mutual Information Neural Estimator
Xiao Lin, Indranil Sur, Samuel A. Nastase +3
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (B…
Understanding Attention and Generalization in Graph Neural Networks
Boris Knyazev, Graham W. Taylor, Mohamed R. Amer
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attenti…
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Boris Knyazev, Xiao Lin, Mohamed R. Amer +1
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful…
Human Motion Modeling using DVGANs
Xiao Lin, Mohamed R. Amer
We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-s…
BitNet: Bit-Regularized Deep Neural Networks
Aswin Raghavan, Mohamed Amer, Sek Chai +1
We present a novel optimization strategy for training neural networks which we call "BitNet". The parameters of neural networks are usually unconstrained and have a dynamic range d…
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Dhanesh Ramachandram, Michal Lisicki, Timothy J. Shields +2
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images. Deep…
GPU Activity Prediction using Representation Learning
Aswin Raghavan, Mohamed Amer, Timothy Shields +2
GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed usin…
Low Precision Neural Networks using Subband Decomposition
Sek Chai, Aswin Raghavan, David Zhang +2
Large-scale deep neural networks (DNN) have been successfully used in a number of tasks from image recognition to natural language processing. They are trained using large training…
Action-Affect Classification and Morphing using Multi-Task Representation Learning
Timothy J. Shields, Mohamed R. Amer, Max Ehrlich +1
Most recent work focused on affect from facial expressions, and not as much on body. This work focuses on body affect analysis. Affect does not occur in isolation. Humans usually c…
Human Social Interaction Modeling Using Temporal Deep Networks
Mohamed R. Amer, Behjat Siddiquie, Amir Tamrakar +4
We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrai…