125 citations · 307 across the 8 of their papers we have counts for
5 papers · 1 filter
Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo
Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accura…
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa +1
We address the problem of biased gradient estimation in deep Boltzmann machines (DBMs). The existing method to obtain an unbiased estimator uses a maximal coupling based on a Gibbs…
A survey of multimodal deep generative models
Masahiro Suzuki, Yutaka Matsuo
Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference o…
Pixyz: a Python library for developing deep generative models
Masahiro Suzuki, Takaaki Kaneko, Yutaka Matsuo
With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research,…
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models
Tadahiro Taniguchi, Tomoaki Nakamura, Masahiro Suzuki +5
This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an exte…