Timezone: »
Poster
Implicit SVD for Graph Representation Learning
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. Meanwhile, Singular Value Decomposition (SVD) can find closed-form solutions to convex problems, using merely a handful of epochs. In this paper, we make GRL more computationally tractable for those with modest hardware. We design a framework that computes SVD of *implicitly* defined matrices, and apply this framework to several GRL tasks. For each task, we derive first-order approximation of a SOTA model, where we design (expensive-to-store) matrix $\mathbf{M}$ and train the model, in closed-form, via SVD of $\mathbf{M}$, without calculating entries of $\mathbf{M}$. By converging to a unique point in one step, and without calculating gradients, our models show competitive empirical test performance over various graphs such as article citation and biological interaction networks. More importantly, SVD can initialize a deeper model, that is architected to be non-linear almost everywhere, though behaves linearly when its parameters reside on a hyperplane, onto which SVD initializes. The deeper model can then be fine-tuned within only a few epochs. Overall, our algorithm trains hundreds of times faster than state-of-the-art methods, while competing on test empirical performance. We open-source our implementation at: https://github.com/samihaija/isvd
Author Information
Sami Abu-El-Haija (USC Information Sciences Institute)
Hesham Mostafa (Intel Corporation)
Marcel Nassar (Intel)
Valentino Crespi (Information Sciences Institute - USC)
Greg Ver Steeg (USC Information Sciences Institute)
Aram Galstyan (USC Information Sciences Institute)
More from the Same Authors
-
2022 : Federated Progressive Sparsification (Purge-Merge-Tune)+ »
Dimitris Stripelis · Umang Gupta · Greg Ver Steeg · Jose-Luis Ambite -
2022 : Bounding the Effects of Continuous Treatments for Hidden Confounders »
Myrl Marmarelis · Greg Ver Steeg · Neda Jahanshad · Aram Galstyan -
2023 Poster: Learning Large Graph Property Prediction via Graph Segment Training »
Kaidi Cao · Phitchaya Phothilimtha · Sami Abu-El-Haija · Dustin Zelle · Yanqi Zhou · Charith Mendis · Jure Leskovec · Bryan Perozzi -
2023 Poster: TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs »
Phitchaya Phothilimtha · Sami Abu-El-Haija · Kaidi Cao · Bahare Fatemi · Charith Mendis · Bryan Perozzi -
2022 Spotlight: Machine Learning on Graphs: A Model and Comprehensive Taxonomy »
Ines Chami · Sami Abu-El-Haija · Bryan Perozzi · Christopher Ré · Kevin Murphy -
2022 : Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science »
Santiago Miret · Kin Long Kelvin Lee · Carmelo Gonzales · Marcel Nassar · Krzysztof Sadowski -
2022 Poster: Machine Learning on Graphs: A Model and Comprehensive Taxonomy »
Ines Chami · Sami Abu-El-Haija · Bryan Perozzi · Christopher Ré · Kevin Murphy -
2022 : TF-GNN Basics (Hands on) »
Sami A Abu-El-Haija -
2022 : GNN Basics »
Sami A Abu-El-Haija -
2022 Expo Workshop: Graph Neural Networks in Tensorflow: A Practical Guide »
Bryan Perozzi · Sami A Abu-El-Haija · Neslihan Bulut · Brandon Mayer -
2021 Poster: Information-theoretic generalization bounds for black-box learning algorithms »
Hrayr Harutyunyan · Maxim Raginsky · Greg Ver Steeg · Aram Galstyan -
2021 Poster: Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling »
Greg Ver Steeg · Aram Galstyan -
2020 Workshop: Deep Learning through Information Geometry »
Pratik Chaudhari · Alexander Alemi · Varun Jog · Dhagash Mehta · Frank Nielsen · Stefano Soatto · Greg Ver Steeg -
2019 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
2019 Poster: Fast structure learning with modular regularization »
Greg Ver Steeg · Hrayr Harutyunyan · Daniel Moyer · Aram Galstyan -
2019 Spotlight: Fast structure learning with modular regularization »
Greg Ver Steeg · Hrayr Harutyunyan · Daniel Moyer · Aram Galstyan -
2019 Poster: Exact Rate-Distortion in Autoencoders via Echo Noise »
Rob Brekelmans · Daniel Moyer · Aram Galstyan · Greg Ver Steeg -
2018 Poster: Invariant Representations without Adversarial Training »
Daniel Moyer · Shuyang Gao · Rob Brekelmans · Aram Galstyan · Greg Ver Steeg -
2018 Poster: Watch Your Step: Learning Node Embeddings via Graph Attention »
Sami Abu-El-Haija · Bryan Perozzi · Rami Al-Rfou · Alexander Alemi -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2016 Poster: Variational Information Maximization for Feature Selection »
Shuyang Gao · Greg Ver Steeg · Aram Galstyan -
2014 Poster: Discovering Structure in High-Dimensional Data Through Correlation Explanation »
Greg Ver Steeg · Aram Galstyan -
2011 Poster: Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs »
Armen Allahverdyan · Aram Galstyan