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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
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
talks
Large Scale Stochastic Optimization with Orthogonality Constraint
Published:
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
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.