7 citations · 24 across the 23 of their papers we have counts for
9 papers · 1 filter
Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search (Full version)
Yuya Sasaki
The number of graph neural network (GNN) architectures has increased rapidly due to the growing adoption of graph analysis. Although we use GNNs in wide application scenarios, it i…
A Simple and Scalable Graph Neural Network for Large Directed Graphs
Seiji Maekawa, Yuya Sasaki, Makoto Onizuka
Node classification is one of the hottest tasks in graph analysis. Though existing studies have explored various node representations in directed and undirected graphs, they have o…
GNN Transformation Framework for Improving Efficiency and Scalability
Seiji Maekawa, Yuya Sasaki, George Fletcher +1
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our…
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi +2
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the m…
An Empirical Study of Personalized Federated Learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets o…
Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Yuya Sasaki, Junya Takayama, Juan Ramón Santana +3
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project i…
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
Seiji Maekawa, Koki Noda, Yuya Sasaki +1
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with l…
FedMe: Federated Learning via Model Exchange
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on…
AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Yuya Sasaki, Kei Harada, Shohei Yamasaki +1
Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality inform…