Atsushi Suzuki

According to our database, Atsushi Suzuki authored at least 17 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces.

Kota Fukuzawa, Atsushi Suzuki, Kenji Yamanishi

IEEE Trans. Inf. Theory, July, 2026

GLoMOT: Efficient Online GNN-based Low-Frame-Rate Multi-Object Tracker.

Yaxuan Hu, Jie Hua, Gang Wu, Yuhong Yang, Atsushi Suzuki, Zhongyuan Wang

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Normalized Maximum Likelihood Code-Length on Riemannian Manifold Data Spaces.

Kota Fukuzawa, Atsushi Suzuki, Kenji Yamanishi

CoRR, August, 2025

MGGA: Universal Perturbations against Deepfake via Multiple Model-based Gradient-Guided Feature Layer Attack.

Zhi Cao, Zhongyuan Wang, Run Wang, Yuhong Yang, Feng Tian, Gang Wu, Atsushi Suzuki

Proceedings of the 1st on Deepfake Forensics Workshop: Detection, 2025

2024

Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models.

Atsushi Suzuki, Kota Fukuzawa, Kenji Yamanishi

CoRR, 2024

2023

Tight and fast generalization error bound of graph embedding in metric space.

Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi

Proceedings of the International Conference on Machine Learning, 2023

Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length.

Ryo Yuki, Atsushi Suzuki, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation.

Zipei Yan, Linchuan Xu, Atsushi Suzuki, Jing Wang, Jiannong Cao, Jun Huang

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior.

Atsushi Suzuki, Kenji Yamanishi

IEEE Trans. Inf. Theory, 2021

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic.

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Error Bound for Hyperbolic Ordinal Embedding.

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

Attributed Subspace Clustering.

Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Hyperbolic Ordinal Embedding.

Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi

Proceedings of The 11th Asian Conference on Machine Learning, 2019

Orderly Subspace Clustering.

Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings.

Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi

CoRR, 2018

Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis.

Atsushi Suzuki, Kenji Yamanishi

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2016

Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding.

Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi

Proceedings of the IEEE 16th International Conference on Data Mining, 2016