Search Results for author: Nick Duffield

Found 19 papers, 8 papers with code

Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data

1 code implementation14 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.

Time Series

Towards Invariant Time Series Forecasting in Smart Cities

no code implementations8 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.

Management Time Series +1

Spectral Convolutional Conditional Neural Processes

1 code implementation19 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.

Inductive Bias Meta-Learning

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

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.

Feature Importance

Adaptive Conditional Quantile Neural Processes

1 code implementation30 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.

Image Inpainting Meta-Learning +1

MoReL: Multi-omics Relational Learning

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.

Graph Embedding Relational Reasoning

Bayesian Graph Contrastive Learning

no code implementations15 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.

Contrastive Learning Self-Supervised Learning +1

BayReL: Bayesian Relational Learning for Multi-omics Data Integration

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.

Data Integration Relational Reasoning +1

Bayesian Graph Neural Networks with Adaptive Connection Sampling

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.

Node Classification

Semi-Implicit Stochastic Recurrent Neural Networks

no code implementations28 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.

Variational Inference

Temporal Network Sampling

no code implementations18 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.

Descriptive Time Series +1

Variational Graph Recurrent Neural Networks

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.

Attribute Dynamic Link Prediction +2

Semi-Implicit Graph Variational Auto-Encoders

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.

Decoder Variational Inference

Adaptive Shrinkage Estimation for Streaming Graphs

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.

Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games

no code implementations14 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.

Attribute

Streaming Network Embedding through Local Actions

no code implementations14 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.

Clustering Multi-class Classification +1

A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games

no code implementations20 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.

Attribute

A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks

no code implementations19 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.

Clustering Management +3

Graphlet Decomposition: Framework, Algorithms, and Applications

no code implementations13 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.

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