Adaptive fuzzy priors for Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2009)

Triply fuzzy function approximation for Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2011)

Noise benefits in the expectation-maximization algorithm: Nem theorems and models. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IJCNN, 2011)

Bayesian Inference With Adaptive Fuzzy Priors and Likelihoods. - Osonde Osoba; Sanya Mitaim; Bart Kosko (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Triply fuzzy function approximation for hierarchical Bayesian inference. - Osonde Osoba; Sanya Mitaim; Bart Kosko (Fuzzy Optim. Decis. Mak., 2012)

Noise benefits in backpropagation and deep bidirectional pre-training. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (IJCNN, 2013)

Noisy hidden Markov models for speech recognition. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (IJCNN, 2013)

Noise-enhanced clustering and competitive learning algorithms. - Osonde Osoba; Bart Kosko (Neural Networks, 2013)

Corrigendum to "Noise enhanced clustering and competitive learning algorithms" [Neural Networks 37 (2013) 132-140]. - Osonde Osoba; Bart Kosko (Neural Networks, 2013)

Noise Benefits in Expectation-Maximization Algorithms. - Osonde Adekorede Osoba (CoRR, 2014)

Noise-enhanced convolutional neural networks. - Kartik Audhkhasi; Osonde Osoba; Bart Kosko (Neural Networks, 2016)

Noisy Expectation-Maximization: Applications and Generalizations. - Osonde Osoba; Bart Kosko (CoRR, 2018)

Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps. - Osonde Osoba; Bart Kosko (CoRR, 2019)

Technocultural Pluralism: A "Clash of Civilizations" in Technology? - Osonde A. Osoba (AIES, 2020)

Steps Towards Value-Aligned Systems. - Osonde A. Osoba; Benjamin Boudreaux; Douglas Yeung (AIES, 2020)

Modeling Agent Behaviors for Policy Analysis via Reinforcement Learning. - Osonde A. Osoba; Raffaele Vardavas; Justin Grana; Rushil Zutshi; Amber Jaycocks (ICMLA, 2020)

Policy-focused Agent-based Modeling using RL Behavioral Models. - Osonde A. Osoba; Raffaele Vardavas; Justin Grana; Rushil Zutshi; Amber Jaycocks (CoRR, 2020)

Deep Generative Modeling in Network Science with Applications to Public Policy Research. - Gavin S. Hartnett; Raffaele Vardavas; Lawrence Baker; Michael Chaykowsky; C. Ben Gibson; Federico Girosi; David P. Kennedy; Osonde A. Osoba (CoRR, 2020)

Noise can speed backpropagation learning and deep bidirectional pretraining. - Bart Kosko; Kartik Audhkhasi; Osonde Osoba (Neural Networks, 2020)

A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games. - Shiva Navabi; Osonde A. Osoba (CDC, 2021)

Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S. - Saikrishna Badrinarayanan; Osonde Osoba; Miao Cheng; Ryan Rogers 002; Sakshi Jain; Rahul Tandra; Natesh S. Pillai (CoRR, 2024)