635 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chuo UniversityJP4 papers
- High Energy Accelerator Research OrganizationJP3 papers
- Ritsumeikan UniversityJP3 papers
- Tamagawa UniversityJP3 papers
- The University of OsakaJP3 papers
- Chiba UniversityJP2 papers
- Institute of Particle and Nuclear Studies2 papers
- Institute of Space and Astronautical ScienceJP2 papers
- Japan Aerospace Exploration AgencyJP2 papers
- Kanazawa UniversityJP2 papers
- Kyoto UniversityJP2 papers
- Nagoya UniversityJP2 papers
6 papers · 1 filter
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in…
Domain-Adversarial and Conditional State Space Model for Imitation Learning
Ryo Okumura, Masashi Okada, Tadahiro Taniguchi
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, a…
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada +3
Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in aut…
Variational Inference MPC for Bayesian Model-based Reinforcement Learning
Masashi Okada, Tadahiro Taniguchi
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making…
Deep Clustered Convolutional Kernels
Minyoung Kim, Luca Rigazio
Deep neural networks have recently achieved state of the art performance thanks to new training algorithms for rapid parameter estimation and new regularization methods to reduce o…
Towards Deep Neural Network Architectures Robust to Adversarial Examples
Shixiang Gu, Luca Rigazio
Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking image…