Bart Kosko - RankMe

103 papers A* 1B 22C 8Misc 3Journal 54Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The Agentic Leash: Extracting Causal Feedback Fuzzy Cognitive Maps with LLMs. CoRR Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
2025	J	`jnl`	Bidirectional Variational Autoencoders. CoRR Bart Kosko, Olaoluwa Adigun
2025	C	`conf`	Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent. ICMLA Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
2025	J	`jnl`	Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent. CoRR Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko
2025	J	`jnl`	Controlled Causal Hallucinations Can Estimate Phantom Nodes in Multiexpert Mixtures of Fuzzy Cognitive Maps. CoRR Akash Kumar Panda, Bart Kosko
2024	B	`conf`	Bidirectional Variational Autoencoders. IJCNN Bart Kosko, Olaoluwa Adigun
2024	C	`conf`	Training Deep Neural Classifiers with Soft Diamond Regularizers. ICMLA Olaoluwa Adigun, Bart Kosko
2024	J	`jnl`	Training Deep Neural Classifiers with Soft Diamond Regularizers. CoRR Olaoluwa Adigun, Bart Kosko
2023	C	`conf`	Bidirectional Backpropagation Autoencoding Networks for Image Compression and Denoising. ICMLA Olaoluwa Adigun, Bart Kosko
2023	B	`conf`	Hidden Priors for Bayesian Bidirectional Backpropagation. SMC Olaoluwa Adigun, Bart Kosko
2023	J	`jnl`	Noise-boosted recurrent backpropagation. Neurocomputing Olaoluwa Adigun, Bart Kosko
2022	C	`conf`	Bayesian Rule Ontologies For XAI Classification and Regression. ICMLA Akash Kumar Panda, Bart Kosko
2022	C	`conf`	Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons. ICMLA Olaoluwa Adigun, Bart Kosko
2022	B	`conf`	Uniform Convergence of Probability Mixtures that Represent Combined Fuzzy Systems. FUZZ-IEEE Bart Kosko
2021	B	`conf`	Bayesian Bidirectional Backpropagation Learning. IJCNN Olaoluwa Adigun, Bart Kosko
2021	B	`conf`	Bayesian Pruned Random Rule Foams for XAI. FUZZ-IEEE Akash Kumar Panda, Bart Kosko
2021	J	`jnl`	Bidirectional Associative Memories: Unsupervised Hebbian Learning to Bidirectional Backpropagation. IEEE Trans. Syst. Man Cybern. Syst. Bart Kosko
2021	C	`conf`	Bidirectional Backpropagation for High-Capacity Blocking Networks. ICMLA Olaoluwa Adigun, Bart Kosko
2021	C	`conf`	Deeper Neural Networks with Non-Vanishing Logistic Hidden Units: NoVa vs. ReLU Neurons. ICMLA Olaoluwa Adigun, Bart Kosko
2021	J	`jnl`	Erratum to "Bidirectional Associative Memories: Unsupervised Hebbian Learning to Bidirectional Backpropagation". IEEE Trans. Syst. Man Cybern. Syst. Bart Kosko
2021	—	`conf`	Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems. NAFIPS Bart Kosko
2020	J	`jnl`	Bidirectional Backpropagation. IEEE Trans. Syst. Man Cybern. Syst. Olaoluwa Adigun, Bart Kosko
2020	B	`conf`	Convergence of Generalized Probability Mixtures That Describe Adaptive Fuzzy Rule-based Systems. FUZZ-IEEE Bart Kosko
2020	B	`conf`	High Capacity Neural Block Classifiers with Logistic Neurons and Random Coding. IJCNN Olaoluwa Adigun, Bart Kosko
2020	J	`jnl`	Noise can speed backpropagation learning and deep bidirectional pretraining. Neural Networks Bart Kosko, Kartik Audhkhasi, Osonde Osoba
2020	—	`conf`	Random Fuzzy-Rule Foams for Explainable AI. NAFIPS Akash Kumar Panda, Bart Kosko
2019	J	`jnl`	Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps. CoRR Osonde Osoba, Bart Kosko
2019	J	`jnl`	Noise-boosted bidirectional backpropagation and adversarial learning. Neural Networks Olaoluwa Adigun, Bart Kosko
2018	J	`jnl`	Additive Fuzzy Systems: From Generalized Mixtures to Rule Continua. Int. J. Intell. Syst. Bart Kosko
2018	J	`jnl`	Noisy Expectation-Maximization: Applications and Generalizations. CoRR Osonde Osoba, Bart Kosko
2018	C	`conf`	Training Generative Adversarial Networks with Bidirectional Backpropagation. ICMLA Olaoluwa Adigun, Bart Kosko
2017	B	`conf`	Generalized mixture representations and combinations for additive fuzzy systems. IJCNN Bart Kosko
2017	B	`conf`	Using noise to speed up video classification with recurrent backpropagation. IJCNN Olaoluwa Adigun, Bart Kosko
2016	J	`jnl`	Noise-enhanced convolutional neural networks. Neural Networks Kartik Audhkhasi, Osonde Osoba, Bart Kosko
2015	—	`ch.`	Additive Fuzzy Systems as Generalized Probability Mixture Models. Fifty Years of Fuzzy Logic and its Applications Bart Kosko
2015	—	`conf`	Using Noise to Speed up Markov Chain Monte Carlo Estimation. INNS Conference on Big Data Brandon Franzke, Bart Kosko
2013	J	`jnl`	Corrigendum to "Noise enhanced clustering and competitive learning algorithms" [Neural Networks 37 (2013) 132-140]. Neural Networks Osonde Osoba, Bart Kosko
2013	B	`conf`	Noise benefits in backpropagation and deep bidirectional pre-training. IJCNN Kartik Audhkhasi, Osonde Osoba, Bart Kosko
2013	J	`jnl`	Noise-enhanced clustering and competitive learning algorithms. Neural Networks Osonde Osoba, Bart Kosko
2013	B	`conf`	Noisy hidden Markov models for speech recognition. IJCNN Kartik Audhkhasi, Osonde Osoba, Bart Kosko
2012	J	`jnl`	Triply fuzzy function approximation for hierarchical Bayesian inference. Fuzzy Optim. Decis. Mak. Osonde Osoba, Sanya Mitaim, Bart Kosko
2011	J	`jnl`	Bayesian Inference With Adaptive Fuzzy Priors and Likelihoods. IEEE Trans. Syst. Man Cybern. Part B Osonde Osoba, Sanya Mitaim, Bart Kosko
2011	J	`jnl`	Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding. IEEE Trans. Signal Process. Ashok Patel, Bart Kosko
2011	B	`conf`	Noise benefits in the expectation-maximization algorithm: Nem theorems and models. IJCNN Osonde Osoba, Sanya Mitaim, Bart Kosko
2011	B	`conf`	Triply fuzzy function approximation for Bayesian inference. IJCNN Osonde Osoba, Sanya Mitaim, Bart Kosko
2010	J	`jnl`	Optimal Mean-Square Noise Benefits in Quantizer-Array Linear Estimation. IEEE Signal Process. Lett. Ashok Patel, Bart Kosko
2010	—	`conf`	Stochastic Resonance Noise Benefits in Markov Chain Monte Carlo Density Estimation. DMIN Brandon Franzke, Bart Kosko
2009	B	`conf`	Adaptive fuzzy priors for Bayesian inference. IJCNN Osonde Osoba, Sanya Mitaim, Bart Kosko
2009	J	`jnl`	Error-probability noise benefits in threshold neural signal detection. Neural Networks Ashok Patel, Bart Kosko
2009	B	`conf`	Neural signal-detection noise benefits based on error probability. IJCNN Ashok Patel, Bart Kosko
2009	J	`jnl`	Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection. IEEE Trans. Signal Process. Ashok Patel, Bart Kosko
2009	Misc	`conf`	Quantizer noise benefits in nonlinear signal detection with alpha-stable channel noise. ICASSP Ashok Patel, Bart Kosko
2008	Misc	`conf`	Optimal noise benefits in Neyman-Pearson signal detection. ICASSP Ashok Patel, Bart Kosko
2008	J	`jnl`	Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise. IEEE Trans. Neural Networks Ashok Patel, Bart Kosko
2007	—	`conf`	Levy Noise Benefits in Neural Signal Detection. ICASSP (3) Ashok Patel, Bart Kosko
2006	B	`conf`	Mutual-Information Noise Benefits in Brownian Models of Continuous and Spiking Neurons. IJCNN Ashok Patel, Bart Kosko
2005	J	`jnl`	Modeling gunshot bruises in soft body armor with an adaptive fuzzy system. IEEE Trans. Syst. Man Cybern. Part B Ian Lee, Bart Kosko, W. French Anderson
2005	B	`conf`	Noise benefits in spiking retinal and sensory neuron models. IJCNN Ashok Patel, Bart Kosko
2005	J	`jnl`	Stochastic resonance in noisy spiking retinal and sensory neuron models. Neural Networks Ashok Patel, Bart Kosko
2004	J	`jnl`	Adaptive stochastic resonance in noisy neurons based on mutual information. IEEE Trans. Neural Networks Sanya Mitaim, Bart Kosko
2004	J	`jnl`	Probable equivalence, superpower sets, and superconditionals. Int. J. Intell. Syst. Bart Kosko
2003	B	`conf`	Almost all noise types can improve the mutual information of threshold neurons that detect subthreshold signals. IJCNN Bart Kosko, Sanya Mitaim
2003	J	`jnl`	Stochastic resonance in noisy threshold neurons. Neural Networks Bart Kosko, Sanya Mitaim
2002	B	`conf`	A conditioning interval based on superconditionals and superpower sets. FUZZ-IEEE Bart Kosko
2001	J	`jnl`	The shape of fuzzy sets in adaptive function approximation. IEEE Trans. Fuzzy Syst. Sanya Mitaim, Bart Kosko
1998	J	`jnl`	Adaptive stochastic resonance. Proc. IEEE Sanya Mitaim, Bart Kosko
1998	J	`jnl`	Global stability of generalized additive fuzzy systems. IEEE Trans. Syst. Man Cybern. Part C Bart Kosko
1998	J	`jnl`	Neural Fuzzy Agents for Profile Learning and Adaptive Object Matching. Presence Teleoperators Virtual Environ. Sanya Mitaim, Bart Kosko
1998	B	`conf`	Neural fuzzy stochastic resonance. SMC Sanya Mitaim, Bart Kosko
1998	J	`jnl`	Special Issue On Intelligent Signal Processing. Proc. IEEE Simon Haykin, Bart Kosko
1998	A*	`conf`	Stochastic Resonance with Adaptive Fuzzy Systems. ICML Sanya Mitaim, Bart Kosko
1997	—	`conf`	Adaptive joint fuzzy sets for function approximation. ICNN Sanya Mitaim, Bart Kosko
1997	—	`conf`	Neural Fuzzy Agents for Database Search. CIA Sanya Mitaim, Bart Kosko
1997	—	`conf`	Neural Fuzzy Agents for Profile Learning and Object Matching. Agents Sanya Mitaim, Bart Kosko
1997	—	`conf`	Neural Fuzzy Agents that Learn a User's Preference Map. ADL Sanya Mitaim, Bart Kosko
1997	—	`conf`	Neural fuzzy agents that learn profiles and search databases. ICNN Sanya Mitaim, Bart Kosko
1997	J	`jnl`	Neural fuzzy motion estimation and compensation. IEEE Trans. Signal Process. Hyun Mun Kim, Bart Kosko
1996	J	`jnl`	Fuzzy function approximation with ellipsoidal rules. IEEE Trans. Syst. Man Cybern. Part B Julie A. Dickerson, Bart Kosko
1996	J	`jnl`	Fuzzy prediction and filtering in impulsive noise. Fuzzy Sets Syst. Hyun Mun Kim, Bart Kosko
1996	J	`jnl`	Fuzzy throttle and brake control for platoons of smart cars. Fuzzy Sets Syst. Hyun Mun Kim, Julie A. Dickerson, Bart Kosko
1995	J	`jnl`	Adaptive fuzzy frequency hopper. IEEE Trans. Commun. Peter J. Pacini, Bart Kosko
1995	J	`jnl`	Optimal fuzzy rules cover extrema. Int. J. Intell. Syst. Bart Kosko
1994	J	`jnl`	Fuzzy Systems as Universal Approximators. IEEE Trans. Computers Bart Kosko
1994	J	`jnl`	The probability monopoly. IEEE Trans. Fuzzy Syst. Bart Kosko
1994	J	`jnl`	Virtual Worlds as Fuzzy Cognitive Maps. Presence Teleoperators Virtual Environ. Julie A. Dickerson, Bart Kosko
1993	J	`jnl`	Addition as fuzzy mutual entropy. Inf. Sci. Bart Kosko
1993	—	`conf`	Fuzzy function learning with covariance ellipsoids. ICNN Julie A. Dickerson, Bart Kosko
1993	—	`conf`	Virtual Worlds as Fuzzy Cognitive Maps. VRAIS Julie A. Dickerson, Bart Kosko
1992	J	`jnl`	Adaptive fuzzy systems for backing up a truck-and-trailer. IEEE Trans. Neural Networks Seong-Gon Kong, Bart Kosko
1992	Misc	`conf`	Fuzzy subimage classification in image sequence coding. ICASSP Seong-Gon Kong, Bart Kosko
1992	—	`book`	Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Bart Kosko
1991	J	`jnl`	Differential competitive learning for centroid estimation and phoneme recognition. IEEE Trans. Neural Networks Seong-Gon Kong, Bart Kosko
1991	J	`jnl`	Stochastic competitive learning. IEEE Trans. Neural Networks Bart Kosko
1990	B	`conf`	Comparison of fuzzy and neural truck backer-upper control systems. IJCNN Seong-Gon Kong, Bart Kosko
1990	B	`conf`	Stochastic competitive learning. IJCNN Bart Kosko
1990	J	`jnl`	Unsupervised learning in noise. IEEE Trans. Neural Networks Bart Kosko
1988	J	`jnl`	Bidirectional associative memories. IEEE Trans. Syst. Man Cybern. Bart Kosko
1988	—	`conf`	Feedback stability and unsupervised learning. ICNN Bart Kosko
1988	J	`jnl`	Hidden patterns in combined and adaptive knowledge networks. Int. J. Approx. Reason. Bart Kosko
1986	J	`jnl`	Counting with Fuzzy Sets. IEEE Trans. Pattern Anal. Mach. Intell. Bart Kosko
1986	J	`jnl`	Fuzzy Cognitive Maps. Int. J. Man Mach. Stud. Bart Kosko
1986	J	`jnl`	Fuzzy entropy and conditioning. Inf. Sci. Bart Kosko
1986	J	`jnl`	Fuzzy knowledge combination. Int. J. Intell. Syst. Bart Kosko

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.