4 citations · 8 across the 3 of their papers we have counts for
6 papers
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory
Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
A neural network model of a differential equation, namely neural ODE, has enabled the learning of continuous-time dynamical systems and probabilistic distributions with high accura…
ChartPointFlow for Topology-Aware 3D Point Cloud Generation
Takumi Kimura, Takashi Matsubara, Kuniaki Uehara
A point cloud serves as a representation of the surface of a three-dimensional (3D) shape. Deep generative models have been adapted to model their variations typically using a map…
Target-Oriented Deformation of Visual-Semantic Embedding Space
Takashi Matsubara
Multimodal embedding is a crucial research topic for cross-modal understanding, data mining, and translation. Many studies have attempted to extract representations from given enti…
Generative adversarial network based on chaotic time series
Makoto Naruse, Takashi Matsubara, Nicolas Chauvet +3
Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator a…
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task
Kenta Hama, Takashi Matsubara, Kuniaki Uehara +1
With the wide development of black-box machine learning algorithms, particularly deep neural network (DNN), the practical demand for the reliability assessment is rapidly rising. O…
Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity
Takashi Matsubara, Kenta Hama, Ryosuke Tachibana +1
Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have…