David van Dijk | DeepAI

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∙ 01/31/2023

Continuous Spatiotemporal Transformers

Modeling spatiotemporal dynamical systems is a fundamental challenge in ...

0 Antonio H. de O. Fonseca, et al. ∙

∙ 10/17/2022

AMPNet: Attention as Message Passing for Graph Neural Networks

Feature-level interactions between nodes can carry crucial information f...

0 Syed Asad Rizvi, et al. ∙

∙ 09/30/2022

Neural Integral Equations

Integral equations (IEs) are functional equations defined through integr...

0 Emanuele Zappala, et al. ∙

∙ 06/28/2022

Neural Integro-Differential Equations

Modeling continuous dynamical systems from discretely sampled observatio...

0 Emanuele Zappala, et al. ∙

∙ 10/12/2020

Permutation invariant networks to learn Wasserstein metrics

Understanding the space of probability measures on a metric space equipp...

21 Arijit Sehanobish, et al. ∙

∙ 06/23/2020

Learning Potentials of Quantum Systems using Deep Neural Networks

Machine Learning has wide applications in a broad range of subjects, inc...

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∙ 06/23/2020

Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks

Graph Neural Networks (GNN) have been extensively used to extract meanin...

0 Arijit Sehanobish, et al. ∙

∙ 06/20/2020

Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy

Word translation is an integral part of language translation. In machine...

0 Antonio H. O. Fonseca, et al. ∙

∙ 02/14/2020

Disease State Prediction From Single-Cell Data Using Graph Attention Networks

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological dis...

0 Neal G. Ravindra, et al. ∙

∙ 02/09/2020

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

It is increasingly common to encounter data from dynamic processes captu...

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∙ 07/10/2019

Coarse Graining of Data via Inhomogeneous Diffusion Condensation

Big data often has emergent structure that exists at multiple levels of ...

2 Nathan Brugnone, et al. ∙

∙ 01/31/2019

Compressed Diffusion

Diffusion maps are a commonly used kernel-based method for manifold lear...

0 Scott Gigante, et al. ∙

∙ 01/25/2019

Finding Archetypal Spaces for Data Using Neural Networks

Archetypal analysis is a type of factor analysis where data is fit by a ...

0 David van Dijk, et al. ∙

∙ 09/30/2018

Graph Spectral Regularization for Neural Network Interpretability

Deep neural networks can learn meaningful representations of data. Howev...

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∙ 02/10/2018

Modeling Dynamics with Deep Transition-Learning Networks

Markov processes, both classical and higher order, are often used to mod...

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