Search Results for author: Truong Son Hy

Found 20 papers, 19 papers with code

DE-HNN: An effective neural model for Circuit Netlist representation

2 code implementations30 Mar 2024 Zhishang Luo, Truong Son Hy, Puoya Tabaghi, Donghyeon Koh, Michael Defferrard, Elahe Rezaei, Ryan Carey, Rhett Davis, Rajeev Jain, Yusu Wang

Using the input and output data of the tools from past designs, one can attempt to build a machine learning model that predicts the outcome of a design in significantly shorter time than running the tool.

Graph Learning

E(3)-Equivariant Mesh Neural Networks

1 code implementation7 Feb 2024 Thuan Trang, Nhat Khang Ngo, Daniel Levy, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy

Triangular meshes are widely used to represent three-dimensional objects.

Symmetry-preserving graph attention network to solve routing problems at multiple resolutions

1 code implementation24 Oct 2023 Cong Dao Tran, Thong Bach, Truong Son Hy

Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods.

Graph Attention

Graph Attention-based Deep Reinforcement Learning for solving the Chinese Postman Problem with Load-dependent costs

1 code implementation24 Oct 2023 Truong Son Hy, Cong Dao Tran

We release our C++ implementations for metaheuristics such as EA, ILS and VNS along with the code for data generation and our generated data at https://github. com/HySonLab/Chinese_Postman_Problem

Graph Attention Traveling Salesman Problem

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

1 code implementation23 Oct 2023 Khanh-Tung Tran, Truong Son Hy, Lili Jiang, Xuan-Son Vu

This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks.

Decision Making Graph Learning +1

Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning

1 code implementation2 Aug 2023 Nhat Khang Ngo, Truong Son Hy

To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with high binding affinities to arbitrary protein targets, guided by a novel multimodal deep neural network built based on graph Transformers as the prior for the generative model.

Drug Discovery Representation Learning

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

1 code implementation29 Jun 2023 Ngoc-Dung Do, Truong Son Hy, Duy Khuong Nguyen

Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model.

Time Series Time Series Forecasting

Neural Multigrid Memory For Computational Fluid Dynamics

1 code implementation21 Jun 2023 Duc Minh Nguyen, Minh Chau Vu, Tuan Anh Nguyen, Tri Huynh, Nguyen Tri Nguyen, Truong Son Hy

Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction.

Computational Efficiency Video Prediction

Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study

1 code implementation12 May 2023 Viet Bach Nguyen, Truong Son Hy, Long Tran-Thanh, Nhung Nghiem

In this work, we propose a novel deep learning architecture named Attention-based Multiresolution Graph Neural Networks (ATMGNN) that learns to combine the spatial graph information, i. e. geographical data, with the temporal information, i. e. timeseries data of number of COVID-19 cases, to predict the future dynamics of the pandemic.

Fast Temporal Wavelet Graph Neural Networks

1 code implementation17 Feb 2023 Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy, Risi Kondor

To facilitate reliable and timely forecast for the human brain and traffic networks, we propose the Fast Temporal Wavelet Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for learning tasks on timeseries data with the underlying graph structure, thanks to the theories of multiresolution analysis and wavelet theory on discrete spaces.

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures

2 code implementations17 Feb 2023 Nhat Khang Ngo, Truong Son Hy, Risi Kondor

Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macromolecules.

Graph Classification Graph Learning +1

Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs

1 code implementation17 Feb 2023 Nhat Khang Ngo, Truong Son Hy, Risi Kondor

Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions.

Link Prediction

On the Connection Between MPNN and Graph Transformer

1 code implementation27 Jan 2023 Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang

Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple benchmarks.

Graph Classification Graph Learning +2

ViDeBERTa: A powerful pre-trained language model for Vietnamese

1 code implementation25 Jan 2023 Cong Dao Tran, Nhut Huy Pham, Anh Nguyen, Truong Son Hy, Tu Vu

This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture.

Language Modelling named-entity-recognition +5

Predicting Drug-Drug Interactions using Deep Generative Models on Graphs

1 code implementation14 Sep 2022 Nhat Khang Ngo, Truong Son Hy, Risi Kondor

However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes.

Link Prediction

HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations

no code implementations31 May 2022 Minh Huynh Nguyen, Nghi D. Q. Bui, Truong Son Hy, Long Tran-Thanh, Tien N. Nguyen

We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet.

Clone Detection Code Classification +2

Temporal Multiresolution Graph Neural Networks For Epidemic Prediction

1 code implementation30 May 2022 Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh, Risi Kondor

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs.

Graph Learning Time Series +1

Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs

1 code implementation2 Nov 2021 Truong Son Hy, Risi Kondor

Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix factorization algorithms in that it does not make a low rank assumption.

Reinforcement Learning (RL)

Multiresolution Equivariant Graph Variational Autoencoder

2 code implementations2 Jun 2021 Truong Son Hy, Risi Kondor

In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner.

Graph Generation Image Generation +3

The general theory of permutation equivarant neural networks and higher order graph variational encoders

1 code implementation8 Apr 2020 Erik Henning Thiede, Truong Son Hy, Risi Kondor

Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector.

Graph Generation Graph Learning +2

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