482 citations · 604 across the 16 of their papers we have counts for
7 papers · 1 filter
Number Theoretic Accelerated Learning of Physics-Informed Neural Networks
Takashi Matsubara, Takaharu Yaguchi
Physics-informed neural networks solve partial differential equations by training neural networks. Since this method approximates infinite-dimensional PDE solutions with finite col…
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities
Takashi Matsubara, Takaharu Yaguchi
Many real-world dynamical systems are associated with first integrals (a.k.a. invariant quantities), which are quantities that remain unchanged over time. The discovery and underst…
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
Zheng Chen, Ziwei Yang, Lingwei Zhu +5
Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recali…
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven…
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…
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…
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…