39 citations · 69 across the 11 of their papers we have counts for
11 papers
Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data
Lei Sun, Yusuke Tanaka, Tomoharu Iwata
Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns. Data-Free Meta-L…
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata +1
The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks…
Meta-Learning for Neural Network-based Temporal Point Processes
Yoshiaki Takimoto, Yusuke Tanaka, Tomoharu Iwata +4
Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is wide…
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
Tomoharu Iwata, Yusuke Tanaka, Naonori Ueda
We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems. The proposed method is designed to meta-learn how to solve…
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata +4
Aggregate data often appear in various fields such as socio-economics and public security. The aggregate data are associated not with points but with supports (e.g., spatial region…
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Maya Okawa, Tomoharu Iwata, Yusuke Tanaka +3
Sequences of events including infectious disease outbreaks, social network activities, and crimes are ubiquitous and the data on such events carry essential information about the u…
Few-shot Learning for Spatial Regression
Tomoharu Iwata, Yusuke Tanaka
We propose a few-shot learning method for spatial regression. Although Gaussian processes (GPs) have been successfully used for spatial regression, they require many observations i…
Probabilistic Optimal Transport based on Collective Graphical Models
Yasunori Akagi, Yusuke Tanaka, Tomoharu Iwata +2
Optimal Transport (OT) is being widely used in various fields such as machine learning and computer vision, as it is a powerful tool for measuring the similarity between probabilit…
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata +4
We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location…
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Maya Okawa, Tomoharu Iwata, Takeshi Kurashima +3
Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields s…
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka +3
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the a…