Search Results for author: Makoto Onizuka

Found 13 papers, 6 papers with code

Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation

1 code implementation EMNLP 2021 Nattapong Tiyajamorn, Tomoyuki Kajiwara, Yuki Arase, Makoto Onizuka

Experimental results on both quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embedding.

Cross-Lingual Semantic Textual Similarity Machine Translation +2

GNN Transformation Framework for Improving Efficiency and Scalability

1 code implementation25 Jul 2022 Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.

Scaling Private Deep Learning with Low-Rank and Sparse Gradients

no code implementations6 Jul 2022 Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi, Yuya Sasaki, Makoto Onizuka

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 magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly.

An Empirical Study of Personalized Federated Learning

1 code implementation27 Jun 2022 Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.

BIG-bench Machine Learning Personalized Federated Learning

Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

1 code implementation21 Jun 2022 Yuya Sasaki, Junya Takayama, Juan Ramón Santana, Shohei Yamasaki, Tomoya Okuno, Makoto Onizuka

Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world.

Graph-to-Sequence

Similarity Search on Computational Notebooks

no code implementations30 Jan 2022 Misato Horiuchi, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search.

FedMe: Federated Learning via Model Exchange

no code implementations15 Oct 2021 Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka

First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.

BIG-bench Machine Learning Federated Learning

AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities

no code implementations16 Aug 2021 Yuya Sasaki, Kei Harada, Shohei Yamasaki, Makoto Onizuka

Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities.

Air Quality Inference

Edit Distance Based Curriculum Learning for Paraphrase Generation

no code implementations ACL 2021 Sora Kadotani, Tomoyuki Kajiwara, Yuki Arase, Makoto Onizuka

Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation.

Machine Translation Paraphrase Generation +1

Fast Subgraph Matching by Exploiting Search Failures

no code implementations28 Dec 2020 Junya Arai, Makoto Onizuka, Yasuhiro Fujiwara, Sotetsu Iwamura

That is, our algorithm generates failure patterns when a partial embedding is found unable to become an isomorphic embedding.

Databases

Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach

no code implementations15 Feb 2020 Yaoshu Wang, Chuan Xiao, Jianbin Qin, Xin Cao, Yifang Sun, Wei Wang, Makoto Onizuka

The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.

Management regression

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