1 code implementation • NeurIPS 2023 • Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Mike Burrows, Charith Mendis, Bryan Perozzi
TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.
Ranked #2 on Runtime ranking on TpuGraphs Layout mean
1 code implementation • 21 Aug 2023 • Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi
We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.
1 code implementation • NeurIPS 2023 • Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi
Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.
1 code implementation • 7 Jul 2022 • Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Pedro Gonnet, Liangze Jiang, Parth Kothari, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
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.
1 code implementation • ICLR Workshop GTRL 2021 • Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan
We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction.
1 code implementation • ICLR 2021 • Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan
We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs.
1 code implementation • ICLR 2021 • Yunhao Ge, Sami Abu-El-Haija, Gan Xin, Laurent Itti
Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc.
1 code implementation • 23 Jul 2020 • Saurabh Singh, Sami Abu-El-Haija, Nick Johnston, Johannes Ballé, Abhinav Shrivastava, George Toderici
We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.
1 code implementation • 7 May 2020 • Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.
no code implementations • 23 Nov 2019 • Kiran Lekkala, Sami Abu-El-Haija, Laurent Itti
Imitation learning has gained immense popularity because of its high sample-efficiency.
no code implementations • 10 Sep 2019 • Chris Piech, Sami Abu-El-Haija
The study is to the best of our knowledge the first on human-language in code and covers 2. 9 million Java repositories.
3 code implementations • 30 Apr 2019 • Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
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 mixing relationships.
1 code implementation • 4 Feb 2019 • Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan
Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump.
1 code implementation • 24 Feb 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Ranked #43 on Node Classification on Pubmed
no code implementations • ICLR 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.
2 code implementations • NeurIPS 2018 • Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).
Ranked #69 on Node Classification on Citeseer
no code implementations • 24 Aug 2017 • Sami Abu-El-Haija
In particular, at every batch, we want to update all trainable tensors, such that the relative change of the L1-norm of the tensors is the same, across all layers of the network, throughout training time.
1 code implementation • 16 May 2017 • Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings.
6 code implementations • 27 Sep 2016 • Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on Action Recognition In Videos on ActivityNet
no code implementations • CVPR 2016 • Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei
In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event.