Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Duffield, Nick"'
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 pre
Externí odkaz:
http://arxiv.org/abs/2406.10419
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. The advancement of deep
Externí odkaz:
http://arxiv.org/abs/2405.05430
Autor:
Mohseni, Peiman, Duffield, Nick
Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes. Their capability to furnish well-calibrated predictions, combined with simple maximu
Externí odkaz:
http://arxiv.org/abs/2404.13182
Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predictions, especially in regression problems, and quick adaptation to new
Externí odkaz:
http://arxiv.org/abs/2305.18777
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. One of critical chall
Externí odkaz:
http://arxiv.org/abs/2203.08149
Autor:
Sharifi, Farinoush, Burris, Mark, Quadrifoglio, Luca, Duffield, Nick, Xu, Xiaodan, Meitiv, Alexander, Xu, Yanzhi Ann
Publikováno v:
In Transportation Research Part D October 2024 135
Autor:
Hasanzadeh, Arman, Armandpour, Mohammadreza, Hajiramezanali, Ehsan, Zhou, Mingyuan, Duffield, Nick, Narayanan, Krishna
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods are incapable of uncertainty quantification for node representations
Externí odkaz:
http://arxiv.org/abs/2112.07823
Autor:
Lin, Binbin, Zou, Lei, Duffield, Nick, Mostafavi, Ali, Cai, Heng, Zhou, Bing, Tao, Jian, Yang, Mingzheng, Mandal, Debayan, Abedin, Joynal
The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed t
Externí odkaz:
http://arxiv.org/abs/2111.03446
Autor:
Hajiramezanali, Ehsan, Hasanzadeh, Arman, Duffield, Nick, Narayanan, Krishna R, Qian, Xiaoning
Publikováno v:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal
Externí odkaz:
http://arxiv.org/abs/2010.05895
Autor:
Hasanzadeh, Arman, Hajiramezanali, Ehsan, Boluki, Shahin, Zhou, Mingyuan, Duffield, Nick, Narayanan, Krishna, Qian, Xiaoning
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The proposed framework not only alleviates over-smoothing and over-fitting
Externí odkaz:
http://arxiv.org/abs/2006.04064