34 citations · 63 across the 9 of their papers we have counts for
6 papers · 1 filter
Multimodal ELBO with Diffusion Decoders
Daniel Wesego, Pedram Rooshenas
Multimodal variational autoencoders have demonstrated their ability to learn the relationships between different modalities by mapping them into a latent representation. Their desi…
Score-Based Multimodal Autoencoder
Daniel Wesego, Pedram Rooshenas
Multimodal Variational Autoencoders (VAEs) represent a promising group of generative models that facilitate the construction of a tractable posterior within the latent space given…
Differential Equation Units: Learning Functional Forms of Activation Functions from Data
MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
MohamadAli Torkamani, Phillip Wallis, Shiv Shankar +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma +1
In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate pred…
The Libra Toolkit for Probabilistic Models
Daniel Lowd, Amirmohammad Rooshenas
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, an…