38 citations · 73 across the 7 of their papers we have counts for
7 papers
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation
Noor Fathima Ghouse, Jens Petersen, Auke Wiggers +2
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models…
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio +1
From ecology to atmospheric sciences, many academic disciplines deal with data characterized by intricate spatio-temporal complexities, the modeling of which often requires special…
Conditional COT-GAN for Video Prediction with Kernel Smoothing
Tianlin Xu, Beatrice Acciaio
Causal Optimal Transport (COT) results from imposing a temporal causality constraint on classic optimal transport problems, which naturally generates a new concept of distances bet…
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Konstantin Klemmer, Sudipan Saha, Matthias Kahl +2
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots…
COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu, Li K. Wenliang, Michael Munn +1
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data. The loss function of this algorithm is formulated using…
Double Generative Adversarial Networks for Conditional Independence Testing
Chengchun Shi, Tianlin Xu, Wicher Bergsma +1
In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning. We propose an inferential proce…
Variational f-divergence Minimization
Mingtian Zhang, Thomas Bird, Raza Habib +2
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent su…