no code implementations • 21 Apr 2024 • Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified.
no code implementations • 6 Mar 2024 • Antonios Valkanas, Yuening Wang, Yingxue Zhang, Mark Coates
Every day the volume of training data is expanding and the number of user interactions is constantly increasing.
2 code implementations • 7 Nov 2023 • Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
no code implementations • 26 Oct 2023 • Mai Zeng, Florence Regol, Mark Coates
Our model is composed of two diffusion processes, one for the time intervals and one for the event types.
no code implementations • 13 Oct 2023 • Florence Regol, Joud Chataoui, Mark Coates
Training an EDNN architecture is challenging as it consists of two intertwined components: the gating mechanism (GM) that controls early-exiting decisions and the intermediate inference modules (IMs) that perform inference from intermediate representations.
no code implementations • 24 Sep 2023 • Muberra Ozmen, Joseph Cotnareanu, Mark Coates
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains.
1 code implementation • 27 May 2023 • Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip Torr, Ser-Nam Lim
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on Node Classification on PATTERN
no code implementations • 2 May 2023 • Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
1 code implementation • 8 Mar 2023 • Florence Regol, Mark Coates
Learning a categorical distribution comes with its own set of challenges.
no code implementations • 6 Feb 2023 • Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates
Contrastive learning has emerged as a premier method for learning representations with or without supervision.
no code implementations • 29 Dec 2022 • Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang, Jie Chuai, Zhitang Chen, Mark Coates, Jianye Hao, Yanhui Geng
This performance relies heavily on the configuration of the network parameters.
no code implementations • 11 Nov 2022 • Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
2 code implementations • 30 Oct 2022 • Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
Ranked #1 on Dynamic Link Prediction on Social Evolution
no code implementations • 28 Oct 2022 • Florence Regol, Anja Kroon, Mark Coates
We validate our evaluation procedure with synthetic experiments on both synthetic generative models and current state-of-the-art categorical generative models.
no code implementations • 21 Sep 2022 • Yitian Zhang, Florence Regol, Antonios Valkanas, Mark Coates
We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.
no code implementations • 4 Aug 2022 • Florence Regol, Soumyasundar Pal, Jianing Sun, Yingxue Zhang, Yanhui Geng, Mark Coates
In this work, we introduce the node copying model for constructing a distribution over graphs.
1 code implementation • 2 Aug 2022 • Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
1 code implementation • 22 Feb 2022 • Soumyasundar Pal, Antonios Valkanas, Florence Regol, Mark Coates
Since a meaningful graph representing dependencies between bags is rarely available, we propose to use a Bayesian GNN framework that can generate a likely graph structure for scenarios where there is uncertainty in the graph or when no graph is available.
1 code implementation • 10 Feb 2022 • Muberra Ozmen, Hao Zhang, Pengyun Wang, Mark Coates
These examples motivate the modelling of multiple types of bi-directional relationships between labels.
Multi-Label Classification Multi-Label Image Classification +4
1 code implementation • 31 Jan 2022 • Segolene Brivet, Faicel Chamroukhi, Mark Coates, Reza Forghani, Peter Savadjiev
In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves.
no code implementations • 10 Nov 2021 • Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark Coates
To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
1 code implementation • 10 Jun 2021 • Soumyasundar Pal, Liheng Ma, Yingxue Zhang, Mark Coates
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
1 code implementation • 17 Apr 2021 • Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
no code implementations • 1 Jan 2021 • Yaochen Hu, Amit Levi, Ishaan Kumar, Yingxue Zhang, Mark Coates
In recent years deep learning has become an important framework for supervised learning.
no code implementations • 27 Nov 2020 • Fatemeh Teimury, Bruno Roy, Juan Sebastián Casallas, David MacDonald, Mark Coates
In this work, we use the power of supervised GNNs for the first time to propose a fully automated UV mapping framework that enables users to replicate their desired seam styles while reducing distortion and seam length.
Graph Learning Graphics
1 code implementation • 25 Aug 2020 • Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates
We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.
1 code implementation • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 • Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
no code implementations • 9 Jul 2020 • Florence Regol, Soumyasundar Pal, Mark Coates
With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks.
no code implementations • ICML 2020 • Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.
no code implementations • 23 Jun 2020 • Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates
A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.
no code implementations • 1 Jan 2020 • Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
1 code implementation • 26 Dec 2019 • Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
no code implementations • 8 Nov 2019 • Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks.
no code implementations • 26 Oct 2019 • Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings.
1 code implementation • 6 Jun 2019 • Cody Mazza-Anthony, Bogdan Mazoure, Mark Coates
We propose two novel estimators based on the Ordered Weighted $\ell_1$ (OWL) norm: 1) The Graphical OWL (GOWL) is a penalized likelihood method that applies the OWL norm to the lower triangle components of the precision matrix.
1 code implementation • 27 Nov 2018 • Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay
Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.
1 code implementation • 21 Sep 2017 • Peter Henderson, Matthew Vertescher, David Meger, Mark Coates
To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic.
no code implementations • 24 Feb 2017 • Hongchao Song, Yunpeng Li, Mark Coates, Aidong Men
One of the most widely used feature extraction method is principle component analysis (PCA).
no code implementations • 19 Feb 2016 • Milad Kharratzadeh, Mark Coates
In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors.
no code implementations • 25 Feb 2015 • Milad Kharratzadeh, Mark Coates
The first matrix linearly transforms the predictors to a set of latent factors, and the second one regresses the responses on these factors.