no code implementations • ICLR 2022 • Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski
Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions.
no code implementations • 10 Jul 2020 • Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski
The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models.
1 code implementation • NeurIPS 2019 • Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Despite many potential applications, existing point process models are limited in their ability to capture complex patterns of interaction.
no code implementations • 23 Jan 2018 • Ifigeneia Apostolopoulou, Diana Marculescu
Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dy- namical systems.