67 citations · 88 across the 12 of their papers we have counts for
14 papers
A Generative Model of Complex Networks Using Graphons and Neural Inverse Operators
Wooseong Choi, Italo'Ivo Lima Dias Pinto, Chen Sun +3
Generative graph models are central to understanding and simulating complex networks. However, existing approaches have complementary strengths and limitations. Mechanistic models…
Neuro-Inspired Hierarchical Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta +6
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration…
Fractional dynamics foster deep learning of COPD stage prediction
Chenzhong Yin, Mihai Udrescu, Gaurav Gupta +6
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends…
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Xiongye Xiao, Defu Cao, Ruochen Yang +5
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve…
Guiding continuous operator learning through Physics-based boundary constraints
Nadim Saad, Gaurav Gupta, Shima Alizadeh +1
Boundary conditions (BCs) are important groups of physics-enforced constraints that are necessary for solutions of Partial Differential Equations (PDEs) to satisfy at specific spat…
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System
Mohamed Ridha Znaidi, Gaurav Gupta, Paul Bogdan
Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference withi…