IEEE

Optimization of Graph Neural Networks Training Using Graph Reordering

Abstract: Graph neural networks (GNNs) are specifically designed for graph-structured data and have gained significant attention. However, training GNN on large-scale graphs remains challenging due to ...
GitHub

Accelerating Training with Neuron Interaction and Nowcasting Networks

We introduce the NiNo model predicting future (nowcasting) parameters by learning neuron interaction in vision and language tasks. We feed c (c=5 by default) past parameter states as input to NiNo and ...
IEEE

Efficient Deep Neural Network Training With a Novel 5.3-Bit Block Floating Point Data Format

Abstract: Low-bit-width data formats offer a promising solution for enhancing the energy efficiency of Deep Neural Network (DNN) training accelerators. In this work, we introduce a novel 5.3-bit data ...
GitHub

Coupling LSTM Neural Networks and State-Space Models through Analytically Tractable Inference

This repo contains the matlab codes to reproduce the results for the paper: Vuong, Nguyen & Goulet (2024), Coupling LSTM Neural Networks and State-Space Models through Analytically Tractable Inference ...