Search Results for author: Marcel Nassar

Found 11 papers, 2 papers with code

Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design

1 code implementation29 Mar 2022 Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp, Pieter Abbeel, Vladimir Stojanović

We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model.

Implicit SVD for Graph Representation Learning

1 code implementation NeurIPS 2021 Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

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 requirements for training, e. g., for calculating gradients via backprop over many data epochs.

Graph Representation Learning

On Local Aggregation in Heterophilic Graphs

no code implementations6 Jun 2021 Hesham Mostafa, Marcel Nassar, Somdeb Majumdar

We also show that homophily is a poor measure of the information in a node's local neighborhood and propose the Neighborhood Information Content(NIC) metric, which is a novel information-theoretic graph metric.

Node Classification

Structured Citation Trend Prediction Using Graph Neural Networks

no code implementations6 Apr 2021 Daniel Cummings, Marcel Nassar

Academic citation graphs represent citation relationships between publications across the full range of academic fields.

Citation Prediction

Permutohedral-GCN: Graph Convolutional Networks with Global Attention

no code implementations2 Mar 2020 Hesham Mostafa, Marcel Nassar

The attention coefficients depend on the Euclidean distance between learnable node embeddings, and we show that the resulting attention-based global aggregation scheme is analogous to high-dimensional Gaussian filtering.

Node Classification

Fully Convolutional Graph Neural Networks using Bipartite Graph Convolutions

no code implementations ICLR 2020 Marcel Nassar, Xin Wang, Evren Tumer

Graph neural networks have been adopted in numerous applications ranging from learning relational representations to modeling data on irregular domains such as point clouds, social graphs, and molecular structures.

Conditional Graph Neural Processes: A Functional Autoencoder Approach

no code implementations13 Dec 2018 Marcel Nassar, Xin Wang, Evren Tumer

Thus, we refer to our model as Conditional Graph Neural Process (CGNP).

Hierarchical Bipartite Graph Convolution Networks

no code implementations17 Nov 2018 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 and social graphs.

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

no code implementations NeurIPS 2017 Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao

Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.

A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments

no code implementations7 Jun 2013 Marcel Nassar, Philip Schniter, Brian L. Evans

We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments.

Cannot find the paper you are looking for? You can Submit a new open access paper.