Application of temporal supervised learning algorithm to generation of natural language. - Ryotaro Kamimura (IJCNN, 1990)

Recognition and restoration of periodic patterns with recurrent neural network. - Ryotaro Kamimura (SPDP, 1990)

Application of the Recurrent Neural Network to the Problem of Language Acquisition. - Ryotaro Kamimura (Conference on Analysis of Neural Network Applications, 1991)

Competitive Learning by Entropy Minimization. - Ryotaro Kamimura (ALT, 1992)

Complexity Term to Generate Explicit Internal Representation in Recurrent Neural Networks. - Ryotaro Kamimura (IFIP Congress (1), 1992)

Minimum entropy methods in neural networks: competition and selective responses by entropy minimization. - Ryotaro Kamimura (ICNN, 1993)

Generation of Internal Representation by alpha. - Ryotaro Kamimura (NIPS, 1993)

Discovery of Linguistic Rules by Hidden Information Maximization. - Ryotaro Kamimura; Taeko Kamimura; Shohachiro Nakanishi (ICNN, 1995)

Minimum α-information strategy for the interpretation of the network behaviors and the improved generalization. - Ryotaro Kamimura; Shohachiro Nakanishi (ICNN, 1995)

Information maximization for feature detection and pattern classification by autoencoders. - Ryotaro Kamimura; Shohachiro Nakanishi (ICNN, 1995)

Kernel Hidden Unit Analysis-Network Size Reduction by Entropy Minimization-. - Ryotaro Kamimura; Shohachiro Nakanishi (IEICE Trans. Inf. Syst., 1995)

Improving Generalization Performance by Information Minimization. - Ryotaro Kamimura; Toshiyuki Takagi; Shohachiro Nakanishi (IEICE Trans. Inf. Syst., 1995)

Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks. - Ryotaro Kamimura; Shohachiro Nakanishi (Neural Process. Lett., 1995)

Constrained information maximization. - Ryotaro Kamimura; Shohachiro Nakanishi (ICNN, 1996)

Kernel feature detector: extracting kernel features by minimizing α-information. - Ryotaro Kamimura; Shohachiro Nakanishi (ICNN, 1996)

Unification of Information Maximization and Minimization. - Ryotaro Kamimura (NIPS, 1996)

D-entropy minimization: integration of mutual information maximization and minimization. - Ryotaro Kamimura (ICNN, 1997)

D-entropy controller for interpretation and generalization. - Ryotaro Kamimura (ICNN, 1997)

Controlling α-entropy with a Neural α-Feature Detector. - Ryotaro Kamimura (Complex Syst., 1997)

Constrained Information Maximization to Control Internal Representatio. - Ryotaro Kamimura (J. Braz. Comput. Soc., 1997)

Information Controller to Maximize and Minimize Information. - Ryotaro Kamimura (Neural Comput., 1997)

Structural Information Control to Improve Generalization. - Ryotaro Kamimura (ICONIP, 1998)

Integrated Information Processors with Multi-functional Components. - Ryotaro Kamimura (ICONIP, 1998)

Minimizing alpha-Information for Generalization and Interpretation. - Ryotaro Kamimura (Algorithmica, 1998)

Mediated and multi-level information processing. - Ryotaro Kamimura (IJCNN, 1999)

Controlling simple structural information to improve generalization performance. - Ryotaro Kamimura (IJCNN, 1999)

Conditional Information Analysis. - Ryotaro Kamimura (IJCNN (1), 2000)

Selective Information Acquisition with Application to Pattern Classification. - Ryotaro Kamimura (IJCNN (1), 2000)

Information theoretic rule discovery in neural networks. - Ryotaro Kamimura; Taeko Kamimura (SMC, 2000)

Cooperative Information Control to Coordinate Competition and Cooperation. - Ryotaro Kamimura; Taeko Kamimura (ICANN, 2001)

Information Maximization and Language Acquisition. - Ryotaro Kamimura; Taeko Kamimura (ICANN, 2001)

Flexible feature discovery and structural information control. - Ryotaro Kamimura; Taeko Kamimura; Osamu Uchida (Connect. Sci., 2001)

Greedy information acquisition algorithm: a new information theoretic approach to dynamic information acquisition in neural networks. - Ryotaro Kamimura; Taeko Kamimura; Haruhiko Takeuchi (Connect. Sci., 2002)

Controlling internal representations by structural information. - Ryotaro Kamimura (Neurocomputing, 2002)

Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning. - Ryotaro Kamimura (ICANN, 2003)

Generating Explicit Self-Organizing Maps by Information Maximization. - Ryotaro Kamimura; Haruhiko Takeuchi (IDEAL, 2003)

Improving Feature Extraction Performance of Greedy Network-Growing Algorithm. - Ryotaro Kamimura; Osamu Uchida (IDEAL, 2003)

Improving generalization by teacher-directed learning. - Ryotaro Kamimura (IJCNN, 2003)

Flexible self-organizing maps by information maximization. - Ryotaro Kamimura; Haruhiko Takeuchi (IJCNN, 2003)

Faithful feature extraction by greedy network-growing algorithm. - Ryotaro Kamimura; Osamu Uchida (IJCNN, 2003)

Information-theoretic Competitive Learning. - Ryotaro Kamimura (Modelling and Simulation, 2003)

Economic Data Analysis and Cooperative Information Control. - Ryotaro Kamimura; Seiki Hashimoto (Modelling and Simulation, 2003)

Progressive Feature Extraction with a Greedy Network-growing Algorithm. - Ryotaro Kamimura (Complex Syst., 2003)

Information theoretic competitive learning in self-adaptive multi-layered networks. - Ryotaro Kamimura (Connect. Sci., 2003)

Teacher-directed learning: information-theoretic competitive learning in supervised multi-layered networks. - Ryotaro Kamimura; Fumihiko Yoshida (Connect. Sci., 2003)

Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units. - Ryotaro Kamimura (Neural Process. Lett., 2003)

One-Epoch Learning for Supervised Information-Theoretic Competitive Learning. - Ryotaro Kamimura (ICONIP, 2004)

Teacher-Directed Learning with Gaussian and Sigmoid Activation Functions. - Ryotaro Kamimura (ICONIP, 2004)

Cost-Sensitive Greedy Network-Growing Algorithm with Gaussian Activation Functions. - Ryotaro Kamimura; Osamu Uchida (ICONIP, 2004)

Teacher-directed information maximization: supervised information-theoretic competitive learning with Gaussian activation functions. - Ryotaro Kamimura (IJCNN, 2004)

Information maximization with Gaussian activation functions to generate explicit self-organizing maps. - Ryotaro Kamimura; Yukiko Maruyama (IJCNN, 2004)

Greedy network-growing by Minkowski distance functions. - Ryotaro Kamimura; Osamu Uchida (IJCNN, 2004)

Improving feature extraction performance of greedy network-growing algorithm by inverse euclidean distance. - Ryotaro Kamimura (Connect. Sci., 2004)

Multi-Layered Greedy Network-Growing Algorithm: Extension of Greedy Network-Growing Algorithm to Multi-Layered Networks. - Ryotaro Kamimura (Int. J. Neural Syst., 2004)

Cooperative information control for self-organizing maps. - Ryotaro Kamimura (Neurocomputing, 2004)

Modular Structure Generation and Its Application to Feature Extraction. - Ryotaro Kamimura (Artificial Intelligence and Applications, 2005)

Extracting Common and Distinctive Features by Cost-Sensitive Information Maximization. - Ryotaro Kamimura (Artificial Intelligence and Applications, 2005)

Maximizing the Ratio of Information to Its Cost in Information Theoretic Competitive Learning. - Ryotaro Kamimura; Sachiko Aida-Hyugaji (ICANN (2), 2005)

Information maximization and cost minimization in information-theoretic competitive learning. - Ryotaro Kamimura (IJCNN, 2005)

Improving information-theoretic competitive learning by accentuated information maximization. - Ryotaro Kamimura (Int. J. Gen. Syst., 2005)

Unifying cost and information in information-theoretic competitive learning. - Ryotaro Kamimura (Neural Networks, 2005)

Self-organizing by Information Maximization: Realizing Self-Organizing Maps by Information-Theoretic Competitive Learning. - Ryotaro Kamimura (ICONIP (1), 2006)

Automatic Inference of Cabinet Approval Ratings by Information-Theoretic Competitive Learning. - Ryotaro Kamimura; Fumihiko Yoshida (ICONIP (2), 2006)

Collective Information-Theoretic Competitive Learning: Emergency of Improved Performance by Collectively Treated Neurons. - Ryotaro Kamimura; Fumihiko Yoshida; Ryozo Kitajima (ICONIP (1), 2006)

Supervised Information Maximization by Weighted Distance. - Ryotaro Kamimura (IJCNN, 2006)

Controlling Excessive Information by Surface Information Criterion for Information-Theoretic Self-Organizing Maps. - Ryotaro Kamimura (SMC, 2006)

Student Survey by Information-Theoretic Competitive Learning. - Ryotaro Kamimura (SMC, 2006)

Cooperative information maximization with Gaussian activation functions for self-organizing maps. - Ryotaro Kamimura (IEEE Trans. Neural Networks, 2006)

Forced information and information loss in information-theoretic competitive learning. - Ryotaro Kamimura (Artificial Intelligence and Applications, 2007)

Interpreting cabinet approval ratings by neural networks. - Ryotaro Kamimura; Fumihiko Yoshida; Ryozo Kitajima (Artificial Intelligence and Applications, 2007)

Combining Hard and Soft Competition in Information-Theoretic Learning. - Ryotaro Kamimura (FOCI, 2007)

Forced Information and Information Loss for a Student Survey Analysis. - Ryotaro Kamimura (FOCI, 2007)

Information-Theoretic Variable Selection in Neural Networks. - Ryotaro Kamimura; Fumihiko Yoshida; Yamashita Toshie; Ryozo Kitajima (FOCI, 2007)

Partially Activated Neural Networks by Controlling Information. - Ryotaro Kamimura (ICANN (1), 2007)

Controlled Competitive Learning: Extending Competitive Learning to Supervised Learning. - Ryotaro Kamimura (IJCNN, 2007)

Forced Information Maximization to Accelerate Information-Theoretic Competitive Learning. - Ryotaro Kamimura; Ryozo Kitajima (IJCNN, 2007)

Information loss to extract distinctive features in competitive learning. - Ryotaro Kamimura (SMC, 2007)

Greedy network-growing algorithm with Minkowski distances. - Ryotaro Kamimura; Osamu Uchida; Seiki Hashimoto (Int. J. Gen. Syst., 2007)

Partially Enhanced Competitive Learning. - Ryotaro Kamimura (ICONIP (2), 2008)

Feature Detection by Structural Enhanced Information. - Ryotaro Kamimura (ICONIP (2), 2008)

Enhanced Visualization by Combing SOM and Mixture Models. - Ryotaro Kamimura (ICONIP (2), 2008)

Collective Activations to Generate Self-Organizing Maps. - Ryotaro Kamimura (ICONIP (2), 2008)

Feature Discovery by Enhancement and Relaxation of Competitive Units. - Ryotaro Kamimura (IDEAL, 2008)

Conditional information and information loss for flexible feature extraction. - Ryotaro Kamimura (IJCNN, 2008)

Interpreting and improving multi-layered networks by free energy-based competitive learning. - Ryotaro Kamimura (SMC, 2008)

Mutual information maximization by free energy-based competitive learning for self-organizing maps. - Ryotaro Kamimura (SMC, 2008)

Information Enhancement Learning: Local Enhanced Information to Detect the Importance of Input Variables in Competitive Learning. - Ryotaro Kamimura (EANN, 2009)

Selective enhancement learning in competitive learning. - Ryotaro Kamimura (IJCNN, 2009)

Self-enhancement learning: Self-supervised and target-creating learning. - Ryotaro Kamimura (IJCNN, 2009)

Self-supervised learning by information enhancement: Target-Generating and Spontaneous learning for Competitive Learning. - Ryotaro Kamimura (SMC, 2009)

Structural Enhanced Information to Detect Features in Competitive Learning. - Ryotaro Kamimura (SMC, 2009)

An information-theoretic approach to feature extraction in competitive learning. - Ryotaro Kamimura (Neurocomputing, 2009)

Relative Relaxation and Weighted Information Loss to Simplify and Stabilize Feature Detection. - Ryotaro Kamimura (J. Adv. Comput. Intell. Intell. Informatics, 2009)

Feature Discovery by Information Loss. - Ryotaro Kamimura (J. Comput., 2009)

Enhancing and Relaxing Competitive Units for Feature Discovery. - Ryotaro Kamimura (Neural Process. Lett., 2009)

Generation of Comprehensible Representations by Supposed Maximum Information. - Ryotaro Kamimura (ICANN (2), 2010)

Pseudo-network Growing for Gradual Interpretation of Input Patterns. - Ryotaro Kamimura (ICONIP (2), 2010)

Information-Theoretic Competitive and Cooperative Learning for Self-Organizing Maps. - Ryotaro Kamimura (ICONIP (2), 2010)

Explicit class structure produced by information-theoretic competitive and cooperative learning. - Ryotaro Kamimura (NaBIC, 2010)

Information enhancement for interpreting competitive learning. - Ryotaro Kamimura (Int. J. Gen. Syst., 2010)

Information-theoretic enhancement learning and its application to visualization of self-organizing maps. - Ryotaro Kamimura (Neurocomputing, 2010)

Relative Information Maximization and Its Application to the Detection of Explicit Class Structure in SOM. - Ryotaro Kamimura (FOCI, 2011)

Explicit Class Structure by Weighted Cooperative Learning. - Ryotaro Kamimura (ICANN (1), 2011)

Individually and Collectively Treated Neurons and its Application to SOM . - Ryotaro Kamimura (IJCCI (NCTA), 2011)

Cooperation control and enhanced class structure in self-organizing maps. - Ryotaro Kamimura (IJCNN, 2011)

Explicit class structure with closeness and similarity between neurons. - Ryotaro Kamimura (NaBIC, 2011)

Structural enhanced information and its application to improved visualization of self-organizing maps. - Ryotaro Kamimura (Appl. Intell., 2011)

Self-enhancement learning: target-creating learning and its application to self-organizing maps. - Ryotaro Kamimura (Biol. Cybern., 2011)

Constrained information maximization by free energy minimization. - Ryotaro Kamimura (Int. J. Gen. Syst., 2011)

Supposed maximum information for comprehensible representations in SOM. - Ryotaro Kamimura (Neurocomputing, 2011)

Selective information enhancement learning for creating interpretable representations in competitive learning. - Ryotaro Kamimura (Neural Networks, 2011)

Separation and Unification of Individuality and Collectivity and Its Application to Explicit Class Structure in Self-Organizing Maps. - Ryotaro Kamimura (ICANN (2), 2012)

Contradiction Resolution for Foreign Exchange Rates Estimation. - Ryotaro Kamimura (IJCCI, 2012)

Interaction of individually and collectively treated neurons for explicit class structure in self-organizing maps. - Ryotaro Kamimura (IJCNN, 2012)

Contradiction resolution and its application to self-organizing maps. - Ryotaro Kamimura (SMC, 2012)

Double enhancement learning for explicit internal representations: unifying self-enhancement and information enhancement to incorporate information on input variables. - Ryotaro Kamimura (Appl. Intell., 2012)

Comprehensibility maximization and humanly comprehensible representations. - Ryotaro Kamimura (Int. J. Gen. Syst., 2012)

Relative information maximization and its application to the extraction of explicit class structure in SOM. - Ryotaro Kamimura (Neurocomputing, 2012)

Dependent input neuron selection in contradiction resolution. - Ryotaro Kamimura (IWCIA, 2013)

Contradiction resolution with explicit and limited evaluation and its application to SOM. - Ryotaro Kamimura (IJCNN, 2013)

Contradiction Resolution with Dependent Input Neuron Selection for Self-Organizing Maps. - Ryotaro Kamimura (SMC, 2013)

Similarity interaction in information-theoretic self-organizing maps. - Ryotaro Kamimura (Int. J. Gen. Syst., 2013)

Repeated comprehensibility maximization in competitive learning. - Ryotaro Kamimura (Neural Comput. Appl., 2013)

Controlling Relations between the Individuality and Collectivity of Neurons and its Application to Self-Organizing Maps. - Ryotaro Kamimura (Neural Process. Lett., 2013)

Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase. - Ryotaro Kamimura (ANNIIP, 2014)

Explicit knowledge extraction in information-theoretic supervised multi-layered SOM. - Ryotaro Kamimura (FOCI, 2014)

Information acquisition performance by supervised information-theoretic self-organizing maps. - Ryotaro Kamimura (NaBIC, 2014)

Information-theoretic multi-layered supervised self-organizing maps for improved prediction performance and explicit internal representation. - Ryotaro Kamimura (SMC, 2014)

Embedded information enhancement for neuron selection in self-organizing maps. - Ryotaro Kamimura; Taeko Kamimura (SMC, 2014)

Input information maximization for improving self-organizing maps. - Ryotaro Kamimura (Appl. Intell., 2014)

Pseudo-potentiality maximization for improved interpretation and generalization in neural networks. - Ryotaro Kamimura (IWCIA, 2015)

Simplified and gradual information control for improving generalization performance of multi-layered neural networks. - Ryotaro Kamimura (IJCNN, 2015)

Selective potentiality maximization for input neuron selection in self-organizing maps. - Ryotaro Kamimura; Ryozo Kitajima (IJCNN, 2015)

Self-Organized Mutual Information Maximization Learning for Improved Generalization Performance. - Ryotaro Kamimura (SMC, 2015)

Self-Organizing Selective Potentiality Learning to Detect Important Input Neurons. - Ryotaro Kamimura (SMC, 2015)

Neural potential learning for tweets classification and interpretation. - Ryozo Kitajima; Ryotaro Kamimura; Osamu Uchida; Fujio Toriumi (SoCPaR, 2015)

Improving visualisation and prediction performance of supervised self-organising map by modified contradiction resolution. - Ryotaro Kamimura (Connect. Sci., 2015)

Accumulative Information Enhancement In The Self-Organizing Maps And Its Application To The Analysis Of Mission Statements. - Ryozo Kitajima; Ryotaro Kamimura (J. Artif. Intell. Soft Comput. Res., 2015)

Simple and Stable Internal Representation by Potential Mutual Information Maximization. - Ryotaro Kamimura (EANN, 2016)

Solving the Vanishing Information Problem with Repeated Potential Mutual Information Maximization. - Ryotaro Kamimura (ICONIP (4), 2016)

Collective Interpretation and Potential Joint Information Maximization. - Ryotaro Kamimura (IIP, 2016)

Repeated potentiality assimilation: Simplifying learning procedures by positive, independent and indirect operation for improving generalization and interpretation. - Ryotaro Kamimura (IJCNN, 2016)

Identifying Important Tweets by Considering the Potentiality of Neurons. - Ryozo Kitajima; Ryotaro Kamimura; Osamu Uchida; Fujio Toriumi (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2016)

Direct Potentiality Assimilation for Improving Multi-Layered Neural Networks. - Ryotaro Kamimura (FedCSIS (Position Papers), 2017)

Supervised semi-autoencoder learning for multi-layered neural networks. - Ryotaro Kamimura; Haruhiko Takeuchi (IFSA-SCIS, 2017)

Selective and cooperative potentiality maximization for improving interpretation and generalization. - Ryotaro Kamimura (IJCNN, 2017)

Potential layer-wise supervised learning for training multi-layered neural networks. - Ryotaro Kamimura (IJCNN, 2017)

Mutual information maximization for improving and interpreting multi-layered neural networks. - Ryotaro Kamimura (SSCI, 2017)

Collective mutual information maximization to unify passive and positive approaches for improving interpretation and generalization. - Ryotaro Kamimura (Neural Networks, 2017)

Autoeconder-Based Excessive Information Generation for Improving and Interpreting Multi-layered Neural Networks. - Ryotaro Kamimura; Haruhiko Takeuchi (IIAI-AAI, 2018)

Excessive, Selective and Collective Information Processing to Improve and Interpret Multi-layered Neural Networks. - Ryotaro Kamimura; Haruhiko Takeuchi (IntelliSys (1), 2018)

Autoeoncoders and Information Augmentation for Improved Generalization and Interpretation in Multi-layered Neural Networks. - Ryotaro Kamimura (ISCBI, 2018)

Local Selective Learning for Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura; Ryozo Kitajima; Hiroyuki Sakai 003 (SCIS&ISIS, 2018)

Correlation-Constrained Mutual Information Maximization for Interpretable Multi-Layered Neural Networks. - Ryotaro Kamimura; Haruhiko Takeuchi (SCIS&ISIS, 2018)

Information-Theoretic Self-compression of Multi-layered Neural Networks. - Ryotaro Kamimura (TPNC, 2018)

Self-Assimilation for Solving Excessive Information Acquisition in Potential Learning. - Ryotaro Kamimura; Tsubasa Kitago (J. Artif. Intell. Soft Comput. Res., 2018)

Information Compression for Intelligible Multi-Layered Neural Networks. - Ryotaro Kamimura (IIAI-AAI, 2019)

Relationship Between Management Policies and Profitability for Second Section-Listed Manufacturing Companies of the Tokyo Stock Exchange (2016 Results). - Ryozo Kitajima; Ryotaro Kamimura; Hiroyuki Sakai 003 (IIAI-AAI, 2019)

Mutual Information Generation for Improving Generalization and Interpretation in Neural Networks. - Ryotaro Kamimura (IJCNN, 2019)

Interpreting Collectively Compressed Multi-Layered Neural Networks. - Ryotaro Kamimura (CIS/RAM, 2019)

Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks. - Ryotaro Kamimura; Haruhiko Takeuchi (Appl. Intell., 2019)

SOM-based information maximization to improve and interpret multi-layered neural networks: From information reduction to information augmentation approach to create new information. - Ryotaro Kamimura (Expert Syst. Appl., 2019)

Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks. - Ryotaro Kamimura (Neurocomputing, 2019)

Supposed Maximum Mutual Information for Improving Generalization and Interpretation of Multi-Layered Neural Networks. - Ryotaro Kamimura (J. Artif. Intell. Soft Comput. Res., 2019)

Cost-Conscious Internal Information Maximization for Disentangling and Interpreting Multi-layered Neural Networks. - Ryotaro Kamimura (HIS, 2020)

Compressing and Interpreting SOM-Based Convolutional Neural Networks. - Ryotaro Kamimura (IEA/AIE, 2020)

Disentangled Representations by Pseudo-Maximum Mutual Information for Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura (IIAI-AAI, 2020)

Bidirectional Estimation of Partially Black-Boxed Layers of SOM-Based Convolutional Neural Networks. - Ryotaro Kamimura (IntelliSys (1), 2020)

Connective Potential Information for Collectively Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura (SSCI, 2020)

Minimum interpretation by autoencoder-based serial and enhanced mutual information production. - Ryotaro Kamimura (Appl. Intell., 2020)

Improving collective interpretation by extended potentiality assimilation for multi-layered neural networks. - Ryotaro Kamimura; Haruhiko Takeuchi (Connect. Sci., 2020)

Cost-conscious mutual information maximization for improving collective interpretation of multi-layered neural networks. - Ryotaro Kamimura (Neurocomputing, 2020)

Forced Selective Information Reduction for Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura; Ryozo Kitajima (IIAI-AAI-Winter, 2021)

Selective Information Control and Network Compression in Multi-layered Neural Networks. - Ryotaro Kamimura (IntelliSys (1), 2021)

Selective Information Control and Layer-Wise Partial Collective Compression for Multi-Layered Neural Networks. - Ryotaro Kamimura (ISDA, 2021)

Partially black-boxed collective interpretation and its application to SOM-based convolutional neural networks. - Ryotaro Kamimura (Neurocomputing, 2021)

Min-Max Cost and Information Control in Multi-layered Neural Networks. - Ryotaro Kamimura; Ryozo Kitajima (FTC (1), 2022)

Serially Disentangled Learning for Multi-Layered Neural Networks. - Ryotaro Kamimura; Ryozo Kitajima (IEA/AIE, 2022)

Multi-level selective potentiality maximization for interpreting multi-layered neural networks. - Ryotaro Kamimura (Appl. Intell., 2022)

Cost-forced and repeated selective information minimization and maximization for multi-layered neural networks. - Ryotaro Kamimura (Int. J. Hybrid Intell. Syst., 2022)

Cost-forced collective potentiality maximization by complementary potentiality minimization for interpreting multi-layered neural networks. - Ryotaro Kamimura (Neurocomputing, 2022)

Repeated Potentiality Augmentation for Multi-layered Neural Networks. - Ryotaro Kamimura (FICC (2), 2023)

Destructive computing with winner-lose-all competition in multi-layered neural networks. - Ryotaro Kamimura (Int. J. Hybrid Intell. Syst., 2023)

Contradiction neutralization for interpreting multi-layered neural networks. - Ryotaro Kamimura (Appl. Intell., 2023)

Impartial competitive learning in multi-layered neural networks. - Ryotaro Kamimura (Connect. Sci., 2023)

Forced and Natural Creative-Prototype Learning for Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura (IIAI-AAI, 2024)

Searching for Idealized Prototype Learning for Interpreting Multi-Layered Neural Networks. - Ryotaro Kamimura (IJCCI, 2024)