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Found 5 papers, 3 papers with code
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
Recent diffusion models for video generation have predominantly utilized attention layers to extract temporal features.
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
We address the problem of biased gradient estimation in deep Boltzmann machines (DBMs).
Langevin Autoencoders for Learning Deep Latent Variable Models
Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE).
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions.
Learning Deep Latent Variable Models via Amortized Langevin Dynamics
Developing a latent variable model and an inference model with neural networks, yields Langevin autoencoders (LAEs), a novel Langevin-based framework for deep generative models.
