84 citations · 115 across the 12 of their papers we have counts for
7 papers · 1 filter
Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Xenia Miscouridou, Samir Bhatt, George Mohler +2
Hawkes processes are point process models that have been used to capture self-excitatory behavior in social interactions, neural activity, earthquakes and viral epidemics. They can…
Modeling and Forecasting Art Movements with CGANs
Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi +2
Conditional Generative Adversarial Networks~(CGAN) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The lat…
Interpreting Deep Neural Networks Through Variable Importance
Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman +2
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited. Unlike previously p…
Multimodal Sentiment Analysis To Explore the Structure of Emotions
Anthony Hu, Seth Flaxman
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the st…
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron +4
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of…
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
Jean-Francois Ton, Seth Flaxman, Dino Sejdinovic +1
The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple line…