Search Results for author:
Found 22 papers, 4 papers with code
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces.
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems.
Excess risk analysis for epistemic uncertainty with application to variational inference
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU).
Permuton-induced Chinese Restaurant Process
Our main contribution is to introduce the notion of permutons into the well-known Chinese restaurant process (CRP) for sequence partitioning: a permuton is a probability measure on $[0, 1]\times [0, 1]$ and can be regarded as a geometric interpretation of the scaling limit of permutations.
Loss function based second-order Jensen inequality and its application to particle variational inference
First, we provide a new second-order Jensen inequality, which has the repulsion term based on the loss function.
Baxter Permutation Process
Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.
Translation Between Waves, wave2wave
The understanding of sensor data has been greatly improved by advanced deep learning methods with big data.
Anomaly Detection with Inexact Labels
We model the anomaly score function by a neural network-based unsupervised anomaly detection method, e. g., autoencoders.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic.
Fully Neural Network based Model for General Temporal Point Processes
We herein propose a novel RNN based model in which the time course of the intensity function is represented in a general manner.
Finding Appropriate Traffic Regulations via Graph Convolutional Networks
In this paper, we propose a method to learn a function that outputs regulation effects given the current traffic situation as inputs.
Unsupervised Object Matching for Relational Data
The estimated latent vectors contain hidden structural information of each object in the given relational dataset.
Partial AUC Maximization via Nonlinear Scoring Functions
In binary classification tasks, accuracy is the most commonly used as a measure of classifier performance.
Few-shot learning of neural networks from scratch by pseudo example optimization
In this paper, we propose a simple but effective method for training neural networks with a limited amount of training data.
Single-epoch supernova classification with deep convolutional neural networks
Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification.
Multi-output Polynomial Networks and Factorization Machines
On recommendation system tasks, we show how to combine our algorithm with a reduction from ordinal regression to multi-output classification and show that the resulting algorithm outperforms simple baselines in terms of ranking accuracy.
Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope.
Instrumentation and Methods for Astrophysics
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks.
Higher-Order Factorization Machines
Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional.
Collapsed Variational Bayes Inference of Infinite Relational Model
The Infinite Relational Model (IRM) is a probabilistic model for relational data clustering that partitions objects into clusters based on observed relationships.
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis
We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks.
Modeling Social Annotation Data with Content Relevance using a Topic Model
We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services.
