Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding

1 code implementation • 28 Dec 2023 • Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa

To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs.

Graph Embedding Link Prediction +1

A Deep Learning Based Cost Model for Automatic Code Optimization

no code implementations • 11 Apr 2021 • Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, Saman Amarasinghe

Unlike previous models, the proposed one works on full programs and does not rely on any heavy feature engineering.

Feature Engineering

TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning

no code implementations • 7 May 2020 • Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous, Fatima Zohra Benhamida, Alex Renda, Jonathan Elliott Frankle, Michael Carbin, Saman Amarasinghe

In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compilers (sparse neural networks here stand for networks that can be accelerated with sparse tensor algebra techniques).