923 citations · 1.6k across the 23 of their papers we have counts for
16 papers · 1 filter
General Geospatial Inference with a Population Dynamics Foundation Model
Mohit Agarwal, Mimi Sun, Chaitanya Kamath +31
Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex rel…
Greedy PIG: Adaptive Integrated Gradients
Kyriakos Axiotis, Sami Abu-al-haija, Lin Chen +2
Deep learning has become the standard approach for most machine learning tasks. While its impact is undeniable, interpreting the predictions of deep learning models from a human pe…
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin +5
Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, so…
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao +4
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal…
Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija +5
Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during trainin…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
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…
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri +4
Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases…
Fast Graph Learning with Unique Optimal Solutions
Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg +1
We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction. For each, we pick a popular…
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi +2
There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categor…
Meta Adaptation using Importance Weighted Demonstrations
Kiran Lekkala, Sami Abu-El-Haija, Laurent Itti
Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dyn…
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5
Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mix…
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi +1
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embedding…
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou +1
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to thes…
Proportionate gradient updates with PercentDelta
Sami Abu-El-Haija
Deep Neural Networks are generally trained using iterative gradient updates. Magnitudes of gradients are affected by many factors, including choice of activation functions and init…
Learning Edge Representations via Low-Rank Asymmetric Projections
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph i…