default search action
Naonori Ueda
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j66]Yuri Aikawa, Naonori Ueda, Toshiyuki Tanaka:
Improving the Efficiency of Training Physics-Informed Neural Networks Using Active Learning. New Gener. Comput. 42(4): 739-760 (2024) - [i17]Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda:
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs. CoRR abs/2402.09018 (2024) - 2023
- [j65]Yuki Yamagishi
, Kazumi Saito, Kazuro Hirahara
Centrality measure and visualization technique for multiple-parent nodes of earthquakes based on correlation-metric. Appl. Netw. Sci. 8(1): 14 (2023) - [j64]Yasuhiro Fujiwara
Efficient Network Representation Learning via Cluster Similarity. Data Sci. Eng. 8(3): 279-291 (2023) - [j63]Kenta Niwa
CoordiNet: Constrained Dynamics Learning for State Coordination Over Graph. IEEE Trans. Signal Inf. Process. over Networks 9: 242-257 (2023) - [c123]Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda:
Efficient Network Representation Learning via Cluster Similarity. DASFAA (3) 2023: 297-307 - [c122]Sota Murakami, Kohei Fujita, Tsuyoshi Ichimura, Takane Hori, Muneo Hori, Maddegedara Lalith, Naonori Ueda:
Development of 3D Viscoelastic Crustal Deformation Analysis Solver with Data-Driven Method on GPU. ICCS (2) 2023: 423-437 - [c121]Bahareh Kalantar, Naonori Ueda, Mohsen Zand, Husam Abdulrasool H. Al-Najjar:
Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields. IGARSS 2023: 5874-5877 - [i16]Tomoharu Iwata, Yusuke Tanaka, Naonori Ueda:
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs. CoRR abs/2310.13270 (2023) - 2022
- [j62]Farman Galeb Saed, Abbas Mohammed Noori
Earthquake-Induced Ground Deformation Assessment via Sentinel-1 Radar Aided at Darbandikhan Town. J. Sensors 2022: 1-11 (2022) - [j61]Seyd Teymoor Seydi
Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model. J. Sensors 2022: 1-20 (2022) - [j60]Seyd Teymoor Seydi, Vahideh Saeidi
Fire-Net: A Deep Learning Framework for Active Forest Fire Detection. J. Sensors 2022: 1-14 (2022) - [j59]Seyd Teymoor Seydi
BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection. Remote. Sens. 14(9): 2214 (2022) - [j58]Erfan Hasanpour Zaryabi, Loghman Moradi
Unboxing the Black Box of Attention Mechanisms in Remote Sensing Big Data Using XAI. Remote. Sens. 14(24): 6254 (2022) - [c120]Hirotaka Hachiya, Kotaro Nagayoshi, Asako Iwaki, Takahiro Maeda, Naonori Ueda, Hiroyuki Fujiwara:
Position-dependent partial convolutions for supervised spatial interpolation. ACML 2022: 420-435 - [c119]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [c118]Masahiro Nakano, Ryo Nishikimi, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Nonparametric Relational Models with Superrectangulation. AISTATS 2022: 8921-8937 - [c117]Yasuhiro Fujiwara, Masahiro Nakano, Atsutoshi Kumagai, Yasutoshi Ida, Akisato Kimura, Naonori Ueda:
Fast Binary Network Hashing via Graph Clustering. IEEE Big Data 2022: 381-388 - [c116]Tsuyoshi Ichimura, Kohei Fujita, Kentaro Koyama, Ryota Kusakabe, Yuma Kikuchi, Takane Hori, Muneo Hori, Lalith Maddegedara, Noriyuki Ohi, Tatsuo Nishiki, Hikaru Inoue, Kazuo Minami, Seiya Nishizawa, Miwako Tsuji
152K-computer-node parallel scalable implicit solver for dynamic nonlinear earthquake simulation. HPC Asia 2022: 18-29 - [c115]Bahareh Kalantar, Seyd Teymoor Seydi, Naonori Ueda, Vahideh Saeidi, Alfian Abdul Halin, Farzin Shabani:
Deep Ensemble Learning for Land Cover Classification Based on Hyperspectral Prisma Image. IGARSS 2022: 3612-3615 - [c114]Bahareh Kalantar, Ojogbane Success Sani
A Deep Learning Approach for Automated Building Outlines Extraction in Compact Urban Environments. IGARSS 2022: 7491-7494 - [c113]Yasue Kishino, Yoshinari Shirai, Koh Takeuchi, Shin Mizutani, Takayuki Suyama, Futoshi Naya, Naonori Ueda:
District Characteristics Analysis with Regional Garbage Amount Estimation Using Vehicle- Mounted Motion Sensors. ISC2 2022: 1-7 - [c112]Yusuke Tanaka, Tomoharu Iwata, Naonori Ueda:
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data. NeurIPS 2022 - [c111]Kohei Fujita, Sota Murakami, Tsuyoshi Ichimura, Takane Hori, Muneo Hori, Maddegedara Lalith, Naonori Ueda:
Scalable Finite-Element Viscoelastic Crustal Deformation Analysis Accelerated with Data-Driven Method. ScalAH@SC 2022: 18-25 - [i15]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - 2021
- [j57]Yusuke Tanaka
Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations. Artif. Intell. 292: 103430 (2021) - [j56]Yuki Yamagishi
Spatio-temporal clustering of earthquakes based on distribution of magnitudes. Appl. Netw. Sci. 6(1): 71 (2021) - [j55]Futoshi Futami
Accelerated Diffusion-Based Sampling by the Non-Reversible Dynamics with Skew-Symmetric Matrices. Entropy 23(8): 993 (2021) - [j54]Diena Al Dogom, Rami Al-Ruzouq
Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE. J. Sensors 2021: 1-25 (2021) - [j53]Mahyat Shafapour Tehrany
Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping. J. Sensors 2021: 6638241:1-6638241:31 (2021) - [j52]Yasuhiro Fujiwara, Sekitoshi Kanai, Yasutoshi Ida, Atsutoshi Kumagai, Naonori Ueda:
Fast Algorithm for Anchor Graph Hashing. Proc. VLDB Endow. 14(6): 916-928 (2021) - [j51]Bahareh Kalantar
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia. Remote. Sens. 13(13): 2638 (2021) - [j50]Ojogbane Success Sani
Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network. Remote. Sens. 13(23): 4803 (2021) - [c110]Hirotaka Hachiya, Yusuke Masumoto, Yuki Mori, Naonori Ueda:
Encoder-decoder-based image transformation approach for integrating precipitation forecasts. ACML 2021: 174-188 - [c109]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Ikko Yamane:
Skew-symmetrically perturbed gradient flow for convex optimization. ACML 2021: 721-736 - [c108]Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Bayesian nonparametric model for arbitrary cubic partitioning. ACML 2021: 1585-1600 - [c107]Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Sekitoshi Kanai, Naonori Ueda:
Fast and Accurate Anchor Graph-based Label Prediction. CIKM 2021: 504-513 - [c106]Yuki Yamagishi, Kazumi Saito, Kazuro Hirahara, Naonori Ueda:
Constructing Weighted Networks of Earthquakes with Multiple-parent Nodes Based on Correlation-Metric. COMPLEX NETWORKS 2021: 487-498 - [c105]Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Naonori Ueda:
Fast Similarity Computation for t-SNE. ICDE 2021: 1691-1702 - [c104]Huda Jamal Jumaah, Bahareh Kalantar, Naonori Ueda, Ojogbane Success Sani
The Effect of War on Land Use Dynamics in Mosul Iraq Using Remote Sensing and GIS Techniques. IGARSS 2021: 6476-6479 - [c103]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [c102]Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Permuton-induced Chinese Restaurant Process. NeurIPS 2021: 27695-27708 - [c101]Tsuyoshi Ichimura, Kohei Fujita, Kentaro Koyama, Yuma Kikuchi, Ryota Kusakabe, Kazuo Minami, Hikaru Inoue, Seiya Nishizawa, Miwako Tsuji
Fast scalable implicit solver with convergence of equation-based modeling and data-driven learning: earthquake city simulation on low-order unstructured finite element. PASC 2021: 12:1-12:12 - [c100]Yuki Yamagishi
Magnitude-Weighted Mean-Shift Clustering with Leave-One-Out Bandwidth Estimation. PRICAI (1) 2021: 347-358 - [c99]Kohei Fujita, Yuma Kikuchi, Tsuyoshi Ichimura, Muneo Hori, Lalith Maddegedara, Naonori Ueda:
GPU Porting of Scalable Implicit Solver with Green's Function-Based Neural Networks by OpenACC. WACCPD@SC 2021: 73-91 - [i14]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR abs/2106.05010 (2021) - 2020
- [j49]Tomoharu Iwata
Anomaly detection with inexact labels. Mach. Learn. 109(8): 1617-1633 (2020) - [j48]Mohamed Barakat A. Gibril
Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification. Remote. Sens. 12(7): 1081 (2020) - [j47]Bahareh Kalantar
Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote. Sens. 12(11): 1737 (2020) - [j46]Kourosh Ahmadi
Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data. Remote. Sens. 12(18): 3019 (2020) - [j45]Bahareh Kalantar
Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote. Sens. 12(21): 3529 (2020) - [j44]Bahareh Kalantar
Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote. Sens. 12(22): 3682 (2020) - [c98]Tomoharu Iwata, Akinori Fujino, Naonori Ueda:
Semi-Supervised Learning for Maximizing the Partial AUC. AAAI 2020: 4239-4246 - [c97]Yuki Yamagishi
Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes. COMPLEX NETWORKS (1) 2020: 627-637 - [c96]Tsuyoshi Ichimura, Kohei Fujita, Takuma Yamaguchi, Muneo Hori, Lalith Wijerathne, Naonori Ueda:
Fast Multi-Step Optimization with Deep Learning for Data-Centric Supercomputing. HP3C 2020: 7-13 - [c95]Takuma Yamaguchi, Tsuyoshi Ichimura, Kohei Fujita, Muneo Hori, Lalith Wijerathne, Naonori Ueda:
Data-Driven Approach to Inversion Analysis of Three-Dimensional Inner Soil Structure via Wave Propagation Analysis. ICCS (3) 2020: 3-17 - [c94]Yasuhiro Fujiwara, Atsutoshi Kumagai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda:
Efficient Algorithm for the b-Matching Graph. KDD 2020: 187-197 - [c93]Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Baxter Permutation Process. NeurIPS 2020 - [c92]Takuma Yamaguchi, Kohei Fujita, Tsuyoshi Ichimura, Akira Naruse, Jack C. Wells
Low-Order Finite Element Solver with Small Matrix-Matrix Multiplication Accelerated by AI-Specific Hardware for Crustal Deformation Computation. PASC 2020: 16:1-16:11 - [c91]Tsuyoshi Ichimura, Kohei Fujita, Muneo Hori, Lalith Maddegedara, Naonori Ueda, Yuma Kikuchi:
A Fast Scalable Iterative Implicit Solver with Green's function-based Neural Networks. ScalA@SC 2020: 61-68 - [i13]Tsuyoshi Okita, Hirotaka Hachiya, Sozo Inoue, Naonori Ueda:
Translation Between Waves, wave2wave. CoRR abs/2007.10394 (2020)
2010 – 2019
- 2019
- [j43]Husam Abdulrasool H. Al-Najjar, Bahareh Kalantar
Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks. Remote. Sens. 11(12): 1461 (2019) - [c90]Yasuhiro Fujiwara, Sekitoshi Kanai, Junya Arai, Yasutoshi Ida, Naonori Ueda:
Efficient Data Point Pruning for One-Class SVM. AAAI 2019: 3590-3597 - [c89]Hirotaka Hachiya, Yu Yamamoto, Kazuro Hirahara, Naonori Ueda:
Adaptive truncated residual regression for fine-grained regression problems. ACML 2019: 868-882 - [c88]Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda:
Fast Random Forest Algorithm via Incremental Upper Bound. CIKM 2019: 2205-2208 - [c87]Bahareh Kalantar, Naonori Ueda, Usman Salihu Lay, Husam Abdulrasool H. Al-Najjar, Alfian Abdul Halin:
Conditioning Factors Determination for Landslide Susceptibility Mapping Using Support Vector Machine Learning. IGARSS 2019: 9626-9629 - [c86]Takuma Otsuka, Hitoshi Shimizu, Tomoharu Iwata, Futoshi Naya, Hiroshi Sawada, Naonori Ueda:
Bayesian Optimization for Crowd Traffic Control Using Multi-Agent Simulation. ITSC 2019: 1981-1988 - [c85]Maya Okawa, Tomoharu Iwata, Takeshi Kurashima, Yusuke Tanaka
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information. KDD 2019: 373-383 - [c84]Takahiro Omi, Naonori Ueda, Kazuyuki Aihara:
Fully Neural Network based Model for General Temporal Point Processes. NeurIPS 2019: 2120-2129 - [i12]Takahiro Omi, Naonori Ueda, Kazuyuki Aihara:
Fully Neural Network based Model for General Temporal Point Processes. CoRR abs/1905.09690 (2019) - [i11]Maya Okawa, Tomoharu Iwata, Takeshi Kurashima, Yusuke Tanaka, Hiroyuki Toda, Naonori Ueda:
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information. CoRR abs/1906.08952 (2019) - [i10]Tomoharu Iwata, Machiko Toyoda, Shotaro Tora, Naonori Ueda:
Anomaly Detection with Inexact Labels. CoRR abs/1909.04807 (2019) - 2018
- [j42]Tomoharu Iwata
Topic Models for Unsupervised Cluster Matching. IEEE Trans. Knowl. Data Eng. 30(4): 786-795 (2018) - [c83]Yasuhiro Fujiwara, Junya Arai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda:
Adaptive Data Pruning for Support Vector Machines. IEEE BigData 2018: 683-692 - [c82]Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda:
Few-shot learning of neural networks from scratch by pseudo example optimization. BMVC 2018: 105 - [c81]Yasue Kishino, Yoshinari Shirai, Koh Takeuchi, Takayuki Suyama, Futoshi Naya, Naonori Ueda:
Regional Garbage Amount Estimation and Analysis Using Car-Mounted Motion Sensors. UbiComp/ISWC Adjunct 2018: 110-113 - [c80]Takuro Yonezawa
Accelerating Urban Science by Crowdsensing with Civil Officers. UbiComp/ISWC Adjunct 2018: 303-306 - [c79]Yusuke Tanaka
Estimating Latent People Flow without Tracking Individuals. IJCAI 2018: 3556-3563 - [c78]Benoît Choffin, Naonori Ueda:
Scaling Bayesian Optimization up to Higher Dimensions: a Review and Comparison of Recent Algorithms. MLSP 2018: 1-6 - [c77]Hitoshi Shimizu, Tatsushi Matsubayashi, Yusuke Tanaka, Tomoharu Iwata, Naonori Ueda, Hiroshi Sawada:
Improving Route Traffic Estimation by Considering Staying Population. PRIMA 2018: 630-637 - [i9]Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda:
Imitation networks: Few-shot learning of neural networks from scratch. CoRR abs/1802.03039 (2018) - [i8]Naonori Ueda, Akinori Fujino:
Partial AUC Maximization via Nonlinear Scoring Functions. CoRR abs/1806.04838 (2018) - [i7]Tomoharu Iwata, Naonori Ueda:
Unsupervised Object Matching for Relational Data. CoRR abs/1810.03770 (2018) - [i6]Tomoharu Iwata, Takuma Otsuka, Hitoshi Shimizu, Hiroshi Sawada, Futoshi Naya, Naonori Ueda:
Finding Appropriate Traffic Regulations via Graph Convolutional Networks. CoRR abs/1810.09712 (2018) - 2017
- [j41]Katsuhiko Ishiguro, Issei Sato, Naonori Ueda:
Averaged Collapsed Variational Bayes Inference. J. Mach. Learn. Res. 18: 1:1-1:29 (2017) - [j40]Tomoharu Iwata, Hitoshi Shimizu, Futoshi Naya, Naonori Ueda:
Estimating People Flow from Spatiotemporal Population Data via Collective Graphical Mixture Models. ACM Trans. Spatial Algorithms Syst. 3(1): 2:1-2:18 (2017) - [c76]Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda:
Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes. AAAI 2017: 132-139 - [c75]Yasue Kishino, Koh Takeuchi, Yoshinari Shirai, Futoshi Naya, Naonori Ueda:
Datafying city: Detecting and accumulating spatio-temporal events by vehicle-mounted sensors. IEEE BigData 2017: 4098-4104 - [c74]Akisato Kimura, Ichiro Takahashi, Masaomi Tanaka, Naoki Yasuda, Naonori Ueda, Naoki Yoshida:
Single-Epoch Supernova Classification with Deep Convolutional Neural Networks. ICDCS Workshops 2017: 354-359 - [c73]Koh Takeuchi, Hisashi Kashima, Naonori Ueda:
Autoregressive Tensor Factorization for Spatio-Temporal Predictions. ICDM 2017: 1105-1110 - [c72]Yasuhiro Fujiwara, Naoki Marumo
SVD-Based Screening for the Graphical Lasso. IJCAI 2017: 1682-1688 - [c71]Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda:
Multi-output Polynomial Networks and Factorization Machines. NIPS 2017: 3349-3359 - [c70]Yasuhiro Fujiwara, Naoki Marumo
Scaling Locally Linear Embedding. SIGMOD Conference 2017: 1479-1492 - [e1]Naonori Ueda, Shinji Watanabe, Tomoko Matsui, Jen-Tzung Chien, Jan Larsen:
27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017, Tokyo, Japan, September 25-28, 2017. IEEE 2017, ISBN 978-1-5090-6341-3 [contents] - [i5]Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda:
Multi-output Polynomial Networks and Factorization Machines. CoRR abs/1705.07603 (2017) - [i4]Akisato Kimura, Ichiro Takahashi, Masaomi Tanaka, Naoki Yasuda, Naonori Ueda, Naoki Yoshida:
Single-epoch supernova classification with deep convolutional neural networks. CoRR abs/1711.11526 (2017) - 2016
- [j39]Tomoharu Iwata, Tsutomu Hirao, Naonori Ueda:
Probabilistic latent variable models for unsupervised many-to-many object matching. Inf. Process. Manag. 52(4): 682-697 (2016) - [j38]Sozo Inoue, Naonori Ueda, Yasunobu Nohara
Recognizing and Understanding Nursing Activities for a Whole Day with a Big Dataset. J. Inf. Process. 24(6): 853-866 (2016) - [c69]Katsuhiko Ishiguro, Issei Sato, Masahiro Nakano, Akisato Kimura, Naonori Ueda:
Infinite Plaid Models for Infinite Bi-Clustering. AAAI 2016: 1701-1708 - [c68]Akinori Fujino, Naonori Ueda:
A Semi-Supervised AUC Optimization Method with Generative Models. ICDM 2016: 883-888 - [c67]Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda:
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. ICML 2016: 850-858 - [c66]Koh Takeuchi, Naonori Ueda:
Graph regularized Non-negative Tensor Completion for spatio-temporal data analysis. IWSC@Middleware 2016: 5:1-5:6 - [c65]Takamichi Toda, Sozo Inoue, Naonori Ueda:
Mobile Activity Recognition through Training Labels with Inaccurate Activity Segments. MobiQuitous 2016: 57-64 - [c64]Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata:
Higher-Order Factorization Machines. NIPS 2016: 3351-3359 - [i3]Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata:
Higher-Order Factorization Machines. CoRR abs/1607.07195 (2016) - [i2]Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda:
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. CoRR abs/1607.08810 (2016) - 2015
- [c63]Sozo Inoue, Naonori Ueda, Yasunobu Nohara
Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset. UbiComp 2015: 1269-1280 - [c62]Morito Matsuoka, Naonori Ueda, Hideyuki Tokuda, Rodger Lea, Luis Muñoz
SmartCities'15: international workshop on smart cities: people, technology and data. UbiComp/ISWC Adjunct 2015: 1509-1513 - [c61]Naonori Ueda, Futoshi Naya, Hitoshi Shimizu, Tomoharu Iwata, Maya Okawa, Hiroshi Sawada:
Real-time and proactive navigation via spatio-temporal prediction. UbiComp/ISWC Adjunct 2015: 1559-1566 - [c60]Yukino Baba, Hisashi Kashima, Yasunobu Nohara
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD 2015: 1681-1690 - [c59]Mathieu Blondel, Akinori Fujino, Naonori Ueda:
Convex Factorization Machines. ECML/PKDD (2) 2015: 19-35 - 2014
- [c58]Mathieu Blondel, Yotaro Kubo, Naonori Ueda:
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. AISTATS 2014: 96-104 - [c57]Takamichi Toda, Naonori Ueda, Sozo Inoue, Shota Tanaka:
Training human activity recognition for labels with inaccurate time stamps. UbiComp Adjunct 2014: 863-872 - [c56]Yasuko Matsubara, Yasushi Sakurai, Naonori Ueda, Masatoshi Yoshikawa:
Fast and Exact Monitoring of Co-Evolving Data Streams. ICDM 2014: 390-399 - [c55]Masahiro Nakano, Katsuhiko Ishiguro, Akisato Kimura, Takeshi Yamada, Naonori Ueda:
Rectangular Tiling Process. ICML 2014: 361-369 - [c54]Mathieu Blondel, Akinori Fujino, Naonori Ueda:
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. ICPR 2014: 1289-1294 - [i1]Katsuhiko Ishiguro, Issei Sato, Naonori Ueda:
Collapsed Variational Bayes Inference of Infinite Relational Model. CoRR abs/1409.4757 (2014) - 2013
- [j37]Akinori Fujino, Naonori Ueda, Masaaki Nagata:
Adaptive semi-supervised learning on labeled and unlabeled data with different distributions. Knowl. Inf. Syst. 37(1): 129-154 (2013) - [j36]Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki
Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data. IEEE Trans. Speech Audio Process. 21(5): 971-982 (2013) - [j35]Tomoharu Iwata, Takeshi Yamada, Naonori Ueda:
Modeling Noisy Annotated Data with Application to Social Annotation. IEEE Trans. Knowl. Data Eng. 25(7): 1601-1613 (2013) - [j34]Xu Sun, Hisashi Kashima, Naonori Ueda:
Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning. IEEE Trans. Knowl. Data Eng. 25(11): 2551-2563 (2013) - [c53]Tomoharu Iwata, Tsutomu Hirao, Naonori Ueda:
Unsupervised Cluster Matching via Probabilistic Latent Variable Models. AAAI 2013: 445-451 - 2012
- [j33]Hirotaka Hachiya, Masashi Sugiyama
Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing 80: 93-101 (2012) - [j32]Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, Naonori Ueda:
Sequential Modeling of Topic Dynamics with Multiple Timescales. ACM Trans. Knowl. Discov. Data 5(4): 19:1-19:27 (2012) - [c52]Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki
Efficient algorithms for multichannel extensions of Itakura-Saito nonnegative matrix factorization. ICASSP 2012: 261-264 - [c51]Naonori Ueda:
Bayesian relational data analysis. KDD 2012: 815 - [c50]Katsuhiko Ishiguro, Naonori Ueda, Hiroshi Sawada:
Subset Infinite Relational Models. AISTATS 2012: 547-555 - 2011
- [j31]Shiro Usui, Nilton Liuji Kamiji
RAST: finding related documents based on triplet similarity. Neural Comput. Appl. 20(7): 993-999 (2011) - [j30]Tomoharu Iwata, Toshiyuki Tanaka, Takeshi Yamada, Naonori Ueda:
Improving Classifier Performance Using Data with Different Taxonomies. IEEE Trans. Knowl. Data Eng. 23(11): 1668-1677 (2011) - [c49]Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki
Formulations and algorithms for multichannel complex NMF. ICASSP 2011: 229-232 - [c48]Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda, Ping Li:
A New Multi-task Learning Method for Personalized Activity Recognition. ICDM 2011: 1218-1223 - [c47]Kazuo Aoyama, Kazumi Saito, Hiroshi Sawada, Naonori Ueda:
Fast approximate similarity search based on degree-reduced neighborhood graphs. KDD 2011: 1055-1063 - [c46]Xu Sun, Hisashi Kashima, Ryota Tomioka, Naonori Ueda:
Large Scale Real-Life Action Recognition Using Conditional Random Fields with Stochastic Training. PAKDD (2) 2011: 222-233 - [c45]Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki
New formulations and efficient algorithms for multichannel NMF. WASPAA 2011: 153-156 - 2010
- [c44]Akinori Fujino, Naonori Ueda, Masaaki Nagata:
A robust semi-supervised classification method for transfer learning. CIKM 2010: 379-388 - [c43]Kazuo Aoyama, Shinji Watanabe
Fast similarity search on a large speech data set with neighborhood graph indexing. ICASSP 2010: 5358-5361 - [c42]Xu Sun, Hisashi Kashima, Takuya Matsuzaki, Naonori Ueda:
Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method. ICDM 2010: 1067-1072 - [c41]Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, Naonori Ueda:
Online multiscale dynamic topic models. KDD 2010: 663-672 - [c40]Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua B. Tenenbaum:
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis. NIPS 2010: 919-927
2000 – 2009
- 2009
- [c39]Daichi Mochihashi, Takeshi Yamada, Naonori Ueda:
Bayesian Unsupervised Word Segmentation with Nested Pitman-Yor Language Modeling. ACL/IJCNLP 2009: 100-108 - [c38]Kazuo Aoyama, Kazumi Saito, Takeshi Yamada, Naonori Ueda:
Fast Similarity Search in Small-World Networks. CompleNet 2009: 185-196 - [c37]Shiro Usui, Nilton Liuji Kamiji
RAST: A Related Abstract Search Tool. ICONIP (2) 2009: 189-195 - [c36]Tomoharu Iwata, Shinji Watanabe, Takeshi Yamada, Naonori Ueda:
Topic Tracking Model for Analyzing Consumer Purchase Behavior. IJCAI 2009: 1427-1432 - [c35]Tomoharu Iwata, Takeshi Yamada, Naonori Ueda:
Modeling Social Annotation Data with Content Relevance using a Topic Model. NIPS 2009: 835-843 - 2008
- [j29]Akinori Fujino, Naonori Ueda, Kazumi Saito:
Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle. IEEE Trans. Pattern Anal. Mach. Intell. 30(3): 424-437 (2008) - [c34]Katsuhiko Ishiguro, Takeshi Yamada, Naonori Ueda:
Simultaneous clustering and tracking unknown number of objects. CVPR 2008 - [c33]Tomoharu Iwata, Takeshi Yamada, Naonori Ueda:
Probabilistic latent semantic visualization: topic model for visualizing documents. KDD 2008: 363-371 - 2007
- [j28]Shiro Usui, Paulito P. Palmes, Kazunori Nagata, Tatsuki Taniguchi, Naonori Ueda:
Keyword extraction, ranking, and organization for the neuroinformatics platform. Biosyst. 88(3): 334-342 (2007) - [j27]Antoine Naud, Shiro Usui, Naonori Ueda, Tatsuki Taniguchi:
Visualization of documents and concepts in neuroinformatics with the 3D-SE viewer. Frontiers Neuroinformatics 1: 7 (2007) - [j26]Akinori Fujino, Naonori Ueda, Kazumi Saito:
A hybrid generative/discriminative approach to text classification with additional information. Inf. Process. Manag. 43(2): 379-392 (2007) - [j25]Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization. Neural Comput. 19(9): 2536-2556 (2007) - [c32]Shuhei Kuwata, Naonori Ueda:
One-shot Collaborative Filtering. CIDM 2007: 300-307 - [c31]Akinori Fujino, Naonori Ueda, Kazumi Saito:
Semi-Supervised Learning for Multi-Component Data Classification. IJCAI 2007: 2754-2759 - [c30]Shiro Usui, Antoine Naud, Naonori Ueda, Tatsuki Taniguchi:
3D-SE Viewer: A Text Mining Tool based on Bipartite Graph Visualization. IJCNN 2007: 1103-1108 - [c29]Manabu Kimura, Kazumi Saito, Naonori Ueda:
Pivot Learning for Efficient Similarity Search. KES (3) 2007: 227-234 - 2006
- [j24]Naonori Ueda, Kazumi Saito:
Parametric mixture model for multitopic text. Syst. Comput. Jpn. 37(2): 56-66 (2006) - [c28]Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griffiths, Takeshi Yamada, Naonori Ueda:
Learning Systems of Concepts with an Infinite Relational Model. AAAI 2006: 381-388 - [c27]Tomoharu Iwata, Kazumi Saito, Naonori Ueda:
Visual nonlinear discriminant analysis for classifier design. ESANN 2006: 283-288 - [c26]Shiro Usui, Paulito P. Palmes, Kazunori Nagata, Tatsuki Taniguchi, Naonori Ueda:
Extracting Keywords from Research Abstracts for the Neuroinformatics Platform Index Tree. IJCNN 2006: 5045-5050 - 2005
- [j23]Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
Selection of Shared-State Hidden Markov Model Structure Using Bayesian Criterion. IEICE Trans. Inf. Syst. 88-D(1): 1-9 (2005) - [c25]Akinori Fujino, Naonori Ueda, Kazumi Saito:
A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design. AAAI 2005: 764-769 - [c24]Akinori Fujino, Naonori Ueda, Kazumi Saito:
A Classifier Design Based on Combining Multiple Components by Maximum Entropy Principle. AIRS 2005: 423-438 - [c23]Masashi Inoue
Retrieving lightly annotated images using image similarities. SAC 2005: 1031-1037 - 2004
- [j22]Masahiro Kimura, Kazumi Saito, Naonori Ueda:
Modeling of growing networks with directional attachment and communities. Neural Networks 17(7): 975-988 (2004) - [j21]Masahiro Kimura, Kazumi Saito, Naonori Ueda:
Modeling network growth with directional attachment and communities. Syst. Comput. Jpn. 35(8): 1-11 (2004) - [j20]Shinji Watanabe
Variational bayesian estimation and clustering for speech recognition. IEEE Trans. Speech Audio Process. 12(4): 365-381 (2004) - [j19]Naonori Ueda, Masashi Inoue
Extended Tied-Mixture HMMs for Both Labeled and Unlabeled Time Series Data. J. VLSI Signal Process. 37(2-3): 189-197 (2004) - [c22]Yuji Kaneda, Naonori Ueda, Kazumi Saito:
Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization. KES 2004: 616-623 - [c21]Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization. NIPS 2004: 617-624 - [c20]Yuji Kaneda, Naonori Ueda, Kazumi Saito:
Document Clustering at NTCIR-4 Workshop: Limiting Search Space of the K-Means Method Using Word Occurrence. NTCIR 2004 - 2003
- [j18]Masashi Inoue
Exploitation of Unlabeled Sequences in Hidden Markov Models. IEEE Trans. Pattern Anal. Mach. Intell. 25(12): 1570-1581 (2003) - [j17]Satoshi Suzuki, Naonori Ueda:
Adaptive clustering using modular learning architecture. Syst. Comput. Jpn. 34(2): 70-80 (2003) - [j16]Masashi Inoue, Naonori Ueda:
Use of unlabeled time series data in hidden Markov models. Syst. Comput. Jpn. 34(13): 1-12 (2003) - [c19]Masahiro Kimura, Kazumi Saito, Naonori Ueda:
Modeling of growing networks with directional attachment and communities. ESANN 2003: 15-20 - [c18]Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
Application of variational Bayesian estimation and clustering to acoustic model adaptation. ICASSP (1) 2003: 568-571 - [c17]Takeshi Yamada, Kazumi Saito, Naonori Ueda:
Cross-Entropy Directed Embedding of Network Data. ICML 2003: 832-839 - 2002
- [j15]Naonori Ueda, Zoubin Ghahramani:
Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks 15(10): 1223-1241 (2002) - [c16]Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
Constructing shared-state hidden Markov models based on a Bayesian approach. INTERSPEECH 2002: 2669-2672 - [c15]Naonori Ueda, Kazumi Saito:
Single-shot detection of multiple categories of text using parametric mixture models. KDD 2002: 626-631 - [c14]Naonori Ueda, Kazumi Saito:
Parametric Mixture Models for Multi-Labeled Text. NIPS 2002: 721-728 - [c13]Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
Application of Variational Bayesian Approach to Speech Recognition. NIPS 2002: 1237-1244 - [c12]Masahiro Kimura, Kazumi Saito, Naonori Ueda:
Modeling of growing networks with communities. NNSP 2002: 189-198 - 2000
- [j14]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. Neural Comput. 12(9): 2109-2128 (2000) - [j13]Naonori Ueda:
Optimal Linear Combination of Neural Networks for Improving Classification Performance. IEEE Trans. Pattern Anal. Mach. Intell. 22(2): 207-215 (2000) - [j12]Naonori Ueda, Ryohei Nakano:
EM algorithm with split and merge operations for mixture models. Syst. Comput. Jpn. 31(5): 1-11 (2000) - [j11]Naonori Ueda:
Optimal linear combination of neural network classifiers based on the minimum classification error criterion. Syst. Comput. Jpn. 31(9): 39-48 (2000) - [j10]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. J. VLSI Signal Process. 26(1-2): 133-140 (2000) - [c11]Kazumi Saito, Naonori Ueda, Shigeru Katagiri, Yutaka Fukai, Hiroshi Fujimaru, Masayuki Fujinawa:
Law discovery from financial data using neural networks. CIFEr 2000: 209-212
1990 – 1999
- 1998
- [j9]Naonori Ueda, Ryohei Nakano:
Deterministic annealing EM algorithm. Neural Networks 11(2): 271-282 (1998) - [c10]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. NIPS 1998: 599-605 - 1997
- [c9]Satoshi Suzuki, Naonori Ueda:
Self-Organization of Feature Columns and its Application to Object Classification. ICONIP (2) 1997: 1166-1169 - 1996
- [c8]Naonori Ueda, Ryohei Nakano:
Generalization error of ensemble estimators. ICNN 1996: 90-95 - 1995
- [j8]Naonori Ueda, Kenji Mase:
Tracking Moving Contours Using Energy-Minimizing Elastic Contour Models. Int. J. Pattern Recognit. Artif. Intell. 9(3): 465-484 (1995) - [j7]Naonori Ueda, Ryohei Nakano:
Competitive and selective learning method for vector quantizer design - Equidistortion principle and its algorithm. Syst. Comput. Jpn. 26(9): 34-49 (1995) - [c7]Naonori Ueda, Ryohei Nakano:
Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap. ICNN 1995: 101-104 - [c6]Ryohei Nakano, Naonori Ueda, Kazumi Saito, Takeshi Yamada:
Parrot-like speaking using optimal vector quantization. ICNN 1995: 2871-2875 - 1994
- [j6]Naonori Ueda, Ryohei Nakano:
A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. Neural Networks 7(8): 1211-1227 (1994) - [c5]Naonori Ueda, Ryohei Nakano:
Deterministic Annealing Variant of the EM Algorithm. NIPS 1994: 545-552 - 1993
- [j5]Satoshi Suzuki, Naonori Ueda, Jack Sklansky:
Graph-Based Thinning for Binary Images. Int. J. Pattern Recognit. Artif. Intell. 7(5): 1009-1030 (1993) - [j4]Naonori Ueda, Satoshi Suzuki:
Learning Visual Models from Shape Contours Using Multiscale Convex/Concave Structure Matching. IEEE Trans. Pattern Anal. Mach. Intell. 15(4): 337-352 (1993) - [j3]Naonori Ueda, Kenji Mase, Yasuhito Suenaga:
A contour tracking method using an elastic contour model and an energy-minimization approach. Syst. Comput. Jpn. 24(8): 59-70 (1993) - [c4]Naonori Ueda, Ryohei Nakano:
A competitive and selective learning method for designing optimal vector quantizers. ICNN 1993: 1444-1450 - 1992
- [j2]Naonori Ueda, Satoshi Suzuki:
Automatic shape model acquisition based on a generalization of convex/concave structure. Syst. Comput. Jpn. 23(1): 89-100 (1992) - [c3]Naonori Ueda, Kenji Mase:
Tracking Moving Contours Using Energy-Minimizing Elastic Contour Models. ECCV 1992: 453-457 - 1991
- [j1]Naonori Ueda, Satoshi Suzuki:
A matching algorithm of deformed planar curves using multiscale convex/concave structures. Syst. Comput. Jpn. 22(5): 94-104 (1991) - [c2]Satoshi Suzuki, Naonori Ueda:
Robust vectorization using graph-based thinning and reliability-based line approximation. CVPR 1991: 494-500 - 1990
- [c1]Naonori Ueda, Satoshi Suzuki:
Automatic shape model acquisition using multiscale segment matching. ICPR (1) 1990: 897-902
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-06 21:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: