257 citations · 543 across the 14 of their papers we have counts for
14 papers
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific…
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Shengyu Feng, Subarna Tripathi, Hesham Mostafa +2
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capt…
Implicit SVD for Graph Representation Learning
Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar +3
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requ…
Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach
Oner Orhan, Vasuki Narasimha Swamy, Thomas Tetzlaff +3
Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (speci…
On Local Aggregation in Heterophilic Graphs
Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity. Traditional GNNs gener…
Structured Citation Trend Prediction Using Graph Neural Networks
Daniel Cummings, Marcel Nassar
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their co…
Permutohedral-GCN: Graph Convolutional Networks with Global Attention
Hesham Mostafa, Marcel Nassar
Graph convolutional networks (GCNs) update a node's feature vector by aggregating features from its neighbors in the graph. This ignores potentially useful contributions from dista…
Conditional Graph Neural Processes: A Functional Autoencoder Approach
Marcel Nassar, Xin Wang, Evren Tumer
We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over a…
Hierarchical Bipartite Graph Convolution Networks
Marcel Nassar
Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds…
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Urs Köster, Tristan J. Webb, Xin Wang +11
Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and in…
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
Marcel Nassar, Philip Schniter, Brian L. Evans
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and…
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning
Jing Lin, Marcel Nassar, Brian L. Evans
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additiv…
Active Bayesian Optimization: Minimizing Minimizer Entropy
Il Memming Park, Marcel Nassar, Mijung Park
The ultimate goal of optimization is to find the minimizer of a target function.However, typical criteria for active optimization often ignore the uncertainty about the minimizer.…