Deep Attentive Variational Inference

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.

Variational Inference

Self-Reflective Variational Autoencoder

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.

Variational Inference

Mutually Regressive Point Processes

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.

Bayesian Inference Point Processes

Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks

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.