1 code implementation • 14 Jun 2024 • Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network prediction models.
no code implementations • 8 May 2024 • Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth.
1 code implementation • 19 Apr 2024 • Peiman Mohseni, Nick Duffield
Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes.
1 code implementation • Advances in Agriculture 2024 • Haoyu Niu, Juan Landivar, Nick Duffield
These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification.
1 code implementation • 30 May 2023 • Peiman Mohseni, Nick Duffield, Bani Mallick, Arman Hasanzadeh
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes.
no code implementations • ICLR 2022 • Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Xiaoning Qian
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems.
no code implementations • 15 Dec 2021 • Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali, Mingyuan Zhou, Nick Duffield, Krishna Narayanan
By learning distributional representations, we provide uncertainty estimates in downstream graph analytics tasks and increase the expressive power of the predictive model.
1 code implementation • NeurIPS 2020 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Xiaoning Qian
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale.
1 code implementation • ICML 2020 • Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
no code implementations • 28 Oct 2019 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.
no code implementations • 18 Oct 2019 • Nesreen K. Ahmed, Nick Duffield, Ryan A. Rossi
In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted.
2 code implementations • NeurIPS 2019 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on Dynamic Link Prediction on Enron Emails
1 code implementation • NeurIPS 2019 • Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
no code implementations • NeurIPS 2020 • Nesreen K. Ahmed, Nick Duffield
We propose a novel adaptive, single-pass sampling framework and unbiased estimators for higher-order network analysis of large streaming networks.
no code implementations • 14 Jan 2019 • Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking.
no code implementations • 14 Nov 2018 • Xi Liu, Ping-Chun Hsieh, Nick Duffield, Rui Chen, Muhe Xie, Xidao Wen
Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges.
no code implementations • 20 Aug 2018 • Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
no code implementations • 19 Nov 2017 • Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R. Narayanan, Byron Chigoy
Building a prediction model for transportation networks is challenging because spatio-temporal dependencies of traffic data in different roads are complex and the graph constructed from road networks is very large.
no code implementations • 13 Jun 2015 • Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield, Theodore L. Willke
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level.