Search Results for author: Sami Abu-El-Haija

Found 17 papers, 12 papers with code

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

Fast Graph Learning with Unique Optimal Solutions

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.

Graph Learning Graph Representation Learning +3

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

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.

Graph Representation Learning

Zero-shot Synthesis with Group-Supervised Learning

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.

End-to-end Learning of Compressible Features

1 code implementation23 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.


Machine Learning on Graphs: A Model and Comprehensive Taxonomy

1 code implementation7 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.

Graph Attention Graph Embedding +2

Human Languages in Source Code: Auto-Translation for Localized Instruction

no code implementations10 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.


MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

3 code implementations30 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.

Node Classification

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

1 code implementation4 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.

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

1 code implementation24 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.

General Classification Node Classification

Network of Graph Convolutional Networks Trained on Random Walks

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.

General Classification Node Classification

Watch Your Step: Learning Node Embeddings via Graph Attention

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).

Graph Attention Graph Embedding +2

Proportionate gradient updates with PercentDelta

no code implementations24 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.

Learning Edge Representations via Low-Rank Asymmetric Projections

1 code implementation16 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.

Link Prediction

Detecting events and key actors in multi-person videos

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.

Event Detection General Classification

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