Skip To Main Content
Mohammed Nowaz Rabbani on Muck Rack

Mohammed Nowaz Rabbani

As seen in: arXiv
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Mohammed Nowaz

Contact Mohammed Nowaz, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Computer Science > Machine Learning

|
By Mohammed Nowaz Rabbani, Kaoutar El
| arXiv
[Submitted on 7 Apr 2026] Title:Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees View a PDF of the paper titled Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees, by Mohammed Nowaz Rabbani Chowdhury and 6 other authors View PDF HTML (experimental) Abstract:Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input.
Open in Who Shared Issue with article?

Computer Science > Computation and Language

|
By Mohammed Nowaz Rabbani, Mohammed Nowshad Ruhani
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (688) Issue with article?

[2603.02633] Robust Heterogeneous Analog-Digital Computing for Mixture-of-Experts Models with Theoretical Generalization Guarantees

|
By Mohammed Nowaz Rabbani, Kaoutar El
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (642) Issue with article?
See all 5 articles