1 code implementation • 9 Apr 2024 • Haotian Chen, Anna Kuzina, Babak Esmaeili, Jakub M Tomczak
We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule.
no code implementations • NeurIPS 2023 • Babak Esmaeili, Robin Walters, Heiko Zimmermann, Jan-Willem van de Meent
Incorporating geometric inductive biases into models can aid interpretability and generalization, but encoding to a specific geometric structure can be challenging due to the imposed topological constraints.
no code implementations • ICLR Workshop EBM 2021 • Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent
In this paper, we propose conjugate energy-based models (CEBMs), a new class of energy-based models that define a joint density over data and latent variables.
no code implementations • NeurIPS 2021 • Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting.
no code implementations • 11 Nov 2019 • Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent
Variational autoencoders optimize an objective that combines a reconstruction loss (the distortion) and a KL term (the rate).
1 code implementation • 22 Dec 2018 • Alican Bozkurt, Babak Esmaeili, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent
This leads to the hypothesis that, for a sufficiently high capacity encoder and decoder, the VAE decoder will perform nearest-neighbor matching according to the coordinates in the latent space.
no code implementations • 12 Dec 2018 • Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent
We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews.
no code implementations • 6 Apr 2018 • Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.