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Stochastic First-Order Learning for Large-Scale Flexibly-Tied Gaussian Mixture Model

Published in , 2024

Recommended citation: Pasande, M., Hosseini, R., & Araabi, B. N. (2024). Stochastic first-order learning for large-scale flexibly tied Gaussian mixture models. Pattern Recognition Letters, 178, 138-144. https://doi.org/10.1016/j.patrec.2023.12.021

Discovering the effective connectome of the brain with dynamic Bayesian DAG learning

Published in , 2024

Recommended citation: Bagheri, A., Pasande, M., Bello, K., Araabi, B. N., & Akhondi-Asl, A. (2024). Discovering the effective connectome of the brain with dynamic Bayesian DAG learning. NeuroImage, 120684. https://doi.org/10.1016/j.neuroimage.2024.120684

Large Scale Stochastic Optimization with Orthogonality Constraint

Published: August 14, 2021

I’ll be talking about my progress in research, building up a foundation of large-scale stochastic optimization algorithms to improve the performance of parameter estimation in gaussian mixture models, and adding the result of our algorithm tests in the case of the generative flow-based model (known as GLOW).

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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