Takashi Matsubara | DeepAI

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∙ 07/26/2023

Good Lattice Training: Physics-Informed Neural Networks Accelerated by Number Theory

Physics-informed neural networks (PINNs) offer a novel and efficient app...

0 Takashi Matsubara, et al. ∙

∙ 11/26/2022

Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model

Semantic editing of images is the fundamental goal of computer vision. A...

0 Takehiro Aoshima, et al. ∙

∙ 10/01/2022

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities

Many real-world dynamical systems are associated with first integrals (a...

0 Takashi Matsubara, et al. ∙

∙ 07/20/2022

Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder

Defining and separating cancer subtypes is essential for facilitating pe...

0 Zheng Chen, et al. ∙

∙ 06/22/2022

Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization

Cancer subtyping is crucial for understanding the nature of tumors and p...

0 Zheng Chen, et al. ∙

∙ 12/28/2021

An Error Analysis Framework for Neural Network Modeling of Dynamical Systems

We propose a theoretical framework for investigating a modeling error ca...

0 Shunpei Terakawa, et al. ∙

∙ 02/22/2021

Universal Approximation Properties of Neural Networks for Energy-Based Physical Systems

In Hamiltonian mechanics and the Landau theory, many physical phenomena ...

0 Yuhan Chen, et al. ∙

∙ 02/19/2021

Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory

A neural network model of a differential equation, namely neural ODE, ha...

0 Takashi Matsubara, et al. ∙

∙ 12/04/2020

ChartPointFlow for Topology-Aware 3D Point Cloud Generation

A point cloud serves as a representation of the surface of a three-dimen...

0 Takumi Kimura, et al. ∙

∙ 10/15/2019

Target-Oriented Deformation of Visual-Semantic Embedding Space

Multimodal embedding is a crucial research topic for cross-modal underst...

0 Takashi Matsubara, et al. ∙

∙ 05/24/2019

Generative adversarial network based on chaotic time series

Generative adversarial network (GAN) is gaining increased importance in ...

0 Makoto Naruse, et al. ∙

∙ 04/09/2019

Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task

With the wide development of black-box machine learning algorithms, part...

0 Kenta Hama, et al. ∙

∙ 11/22/2018

Data Augmentation using Random Image Cropping and Patching for Deep CNNs

Deep convolutional neural networks (CNNs) have achieved remarkable resul...

0 Ryo Takahashi, et al. ∙

∙ 07/16/2018

Anomaly Machine Component Detection by Deep Generative Model with Unregularized Score

One of the most common needs in manufacturing plants is rejecting produc...

2 Takashi Matsubara, et al. ∙

∙ 12/18/2017

Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis

Accurate diagnosis of psychiatric disorders plays a critical role in imp...

0 Takashi Matsubara, et al. ∙

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