Shinichi Nakajima

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 62

Top Topics

Exact Inference

Labeled Samples

Variational Bayes

Gaussian Process

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
Physics-Informed Bayesian Optimization of Variational Quantum Circuits. CoRR (2024)
Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas W. A. Gebauer
Molecular relaxation by reverse diffusion with time step prediction. CoRR (2024)
Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
Solution Simplex Clustering for Heterogeneous Federated Learning. CoRR (2024)
Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. CoRR (2023)
Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
Labeling Neural Representations with Inverse Recognition. CoRR (2023)
Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima
Relevant Walk Search for Explaining Graph Neural Networks. ICML (2023)
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima
Local Function Complexity for Active Learning via Mixture of Gaussian Processes. Trans. Mach. Learn. Res. 2023 (2023)
Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
Labeling Neural Representations with Inverse Recognition. NeurIPS (2023)
Gabriel Nobis, Marco Aversa, Maximilian Springenberg, Michael Detzel, Stefano Ermon, Shinichi Nakajima, Roderick Murray-Smith, Sebastian Lapuschkin, Christoph Knochenhauer, Luis Oala, Wojciech Samek
Generative Fractional Diffusion Models. CoRR (2023)
Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Langevin Cooling for Unsupervised Domain Translation. IEEE Trans. Neural Networks Learn. Syst. 34 (10) (2023)
David Lassner, Stephanie Brandl, Anne Baillot, Shinichi Nakajima
Domain-Specific Word Embeddings with Structure Prediction. Trans. Assoc. Comput. Linguistics 11 (2023)
Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima
Physics-Informed Bayesian Optimization of Variational Quantum Circuits. NeurIPS (2023)
Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. ICML (2022)
Stephanie Brandl, David Lassner, Anne Baillot, Shinichi Nakajima
Domain-Specific Word Embeddings with Structure Prediction. CoRR (2022)
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11) (2022)
Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel
Path-Gradient Estimators for Continuous Normalizing Flows. ICML (2022)
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
Path-Gradient Estimators for Continuous Normalizing Flows. CoRR (2022)
Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Vincent Barriac, Thomas Zinner
ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques. IEEE Trans. Netw. Serv. Manag. 19 (3) (2022)
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows. CoRR (2022)
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows. Mach. Learn. Sci. Technol. 3 (4) (2022)
Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima
Mixture-of-experts VAEs can disregard variation in surjective multimodal data. CoRR (2022)
Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI (2022)
Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
Visualizing the diversity of representations learned by Bayesian neural networks. CoRR (2022)
Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
Explaining Bayesian Neural Networks. CoRR (2021)
Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR (2021)
Vignesh Srinivasan, Csaba Rohrer, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Robustifying models against adversarial attacks by Langevin dynamics. Neural Networks 137 (2021)
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. CoRR (2021)
Danny Panknin, Shinichi Nakajima, Klaus-Robert Müller
Optimal Sampling Density for Nonparametric Regression. CoRR (2021)
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. CoRR (2020)
Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Langevin Cooling for Domain Translation. CoRR (2020)
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Towards Best Practice in Explaining Neural Network Decisions with LRP. IJCNN (2020)
David Lassner, Anne Baillot, Sergej Dogadov, Klaus-Robert Müller, Shinichi Nakajima
Automatic Identification of Types of Alterations in Historical Manuscripts. CoRR (2020)
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR (2020)
Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
Optimizing for Measure of Performance in Max-Margin Parsing. IEEE Trans. Neural Networks Learn. Syst. 31 (7) (2020)
Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR (2020)
Susanna Schwarzmann, Clarissa Cassales Marquezan, Riccardo Trivisonno, Shinichi Nakajima, Thomas Zinner
Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks. ICC (2020)
Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Benign Examples: Imperceptible Changes Can Enhance Image Translation Performance. AAAI (2020)
Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. EUSIPCO (2019)
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Pan Kessel
Asymptotically Unbiased Generative Neural Sampling. CoRR (2019)
Danny Panknin, Shinichi Nakajima, Thanh Binh Bui, Klaus-Robert Müller
Local Bandwidth Estimation via Mixture of Gaussian Processes. CoRR (2019)
Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution. CoRR (2019)
Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. AISTATS (2019)
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Towards best practice in explaining neural network decisions with LRP. CoRR (2019)
Alexander Bauer, Shinichi Nakajima
Worst-Case Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies. CoRR (2019)
Kim Nicoli, Pan Kessel, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, Shinichi Nakajima
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling. CoRR (2019)
Vignesh Srinivasan, Arturo Marbán, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Counterstrike: Defending Deep Learning Architectures Against Adversarial Samples by Langevin Dynamics with Supervised Denoising Autoencoder. CoRR (2018)
Nico Görnitz, Luiz Alberto Lima, Klaus-Robert Müller, Marius Kloft, Shinichi Nakajima
Support Vector Data Descriptions and k-Means Clustering: One Class? IEEE Trans. Neural Networks Learn. Syst. 29 (9) (2018)
Jacob R. Kauffmann, Grégoire Montavon, Luiz Alberto Lima, Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. CoRR (2018)
Stephan Kaltenstadler, Shinichi Nakajima, Klaus-Robert Müller, Wojciech Samek
Wasserstein Stationary Subspace Analysis. IEEE J. Sel. Top. Signal Process. 12 (6) (2018)
Hannah Marienwald, Wikor Pronobis, Klaus-Robert Müller, Shinichi Nakajima
Tight Bound of Incremental Cover Trees for Dynamic Diversification. CoRR (2018)
Nico Görnitz, Luiz Alberto Lima, Luiz Eduardo Varella, Klaus-Robert Müller, Shinichi Nakajima
Transductive Regression for Data With Latent Dependence Structure. IEEE Trans. Neural Networks Learn. Syst. 29 (7) (2018)
Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs. CoRR (2017)
János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz
Minimizing Trust Leaks for Robust Sybil Detection. ICML (2017)
Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus-Robert Müller
Optimizing for Measure of Performance in Max-Margin Parsing. CoRR (2017)
Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima
Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops (2017)
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
Sparse probit linear mixed model. Mach. Learn. 106 (9-10) (2017)
Luiz Alberto Lima, Nico Görnitz, Luiz Eduardo Varella, Marley M. B. R. Vellasco, Klaus-Robert Müller, Shinichi Nakajima
Porosity estimation by semi-supervised learning with sparsely available labeled samples. Comput. Geosci. 106 (2017)
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller
Efficient Exact Inference With Loss Augmented Objective in Structured Learning. IEEE Trans. Neural Networks Learn. Syst. 28 (11) (2017)
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI (2016)
Wikor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Müller, Shinichi Nakajima
Sharing Hash Codes for Multiple Purposes. CoRR (2016)
Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Görnitz, Feiyu Xu
SynsetRank: Degree-adjusted Random Walk for Relation Identification. CoRR (2016)
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
Sparse Estimation in a Correlated Probit Model. CoRR (2015)
Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. 16 (2015)
S. Derin Babacan, Shinichi Nakajima, Minh N. Do
Bayesian Group-Sparse Modeling and Variational Inference. IEEE Trans. Signal Process. 62 (11) (2014)
Shinichi Nakajima, Masashi Sugiyama
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS (2014)
Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS (2014)
Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS (2013)
Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
Variational Bayesian sparse additive matrix factorization. Mach. Learn. 92 (2-3) (2013)
Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
Parametric Task Learning. NIPS (2013)
Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka
Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. 14 (1) (2013)
Pablo Ruiz, Javier Mateos, María C. Cárdenas, Shinichi Nakajima, Rafael Molina, Aggelos K. Katsaggelos
Light field acquisition from blurred observations using a programmable coded aperture camera. EUSIPCO (2013)
S. Derin Babacan, Shinichi Nakajima, Minh N. Do
Probabilistic Low-Rank Subspace Clustering. NIPS (2012)
Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS (2012)
Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. ACML (2012)
Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. ICML (2011)
Takeshi Matsuo, Shinichi Nakajima
Attribute-Based MED System with Word Histograms. TRECVID (2011)
Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe
Insights from Classifying Visual Concepts with Multiple Kernel Learning CoRR (2011)
Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. NIPS (2011)
Shinichi Nakajima, Masashi Sugiyama
Theoretical Analysis of Bayesian Matrix Factorization. J. Mach. Learn. Res. 12 (2011)
Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS (2010)
Takeshi Matsuo, Shinichi Nakajima
Nikon Multimedia Event Detection System. TRECVID (2010)
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. 78 (1-2) (2010)
Shinichi Nakajima, Masashi Sugiyama
Implicit Regularization in Variational Bayesian Matrix Factorization. ICML (2010)
Nils Plath, Marc Toussaint, Shinichi Nakajima
Multi-class image segmentation using conditional random fields and global classification. ICML (2009)
Shinichi Nakajima, Masashi Sugiyama
Analysis of Variational Bayesian Matrix Factorization. PAKDD (2009)
Masashi Sugiyama, Shinichi Nakajima
Pool-based active learning in approximate linear regression. Mach. Learn. 75 (3) (2009)
Marius Kloft, Shinichi Nakajima, Ulf Brefeld
Feature Selection for Density Level-Sets. ECML/PKDD (1) (2009)
Motoaki Kawanabe, Shinichi Nakajima, Alexander Binder
A procedure of adaptive kernel combination with kernel-target alignment for object classification. CIVR (2009)
Masashi Sugiyama, Shinichi Nakajima
Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) (2008)
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD (2008)
Shinichi Nakajima, Sumio Watanabe
Generalization Error of Automatic Relevance Determination. ICANN (1) (2007)
Shinichi Nakajima, Sumio Watanabe
Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance. Neural Comput. 19 (4) (2007)
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. NIPS (2007)
Shinichi Nakajima, Sumio Watanabe
Generalization Performance of Subspace Bayes Approach in Linear Neural Networks. IEICE Trans. Inf. Syst. (3) (2006)
Shinichi Nakajima, Sumio Watanabe
Analytic Solution of Hierarchical Variational Bayes in Linear Inverse Problem. ICANN (2) (2006)
Shingo Takamatsu, Shinichi Nakajima, Sumio Watanabe
Localized Bayes Estimation for Non-identifiable Models. ICONIP (1) (2006)
Shinichi Nakajima, Sumio Watanabe
Generalization Error of Linear Neural Networks in an Empirical Bayes Approach. IJCAI (2005)