Ali Taghibakhshi

According to our database, Ali Taghibakhshi authored at least 13 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs.

Ali Taghibakhshi, Sharath Turuvekere Sreenivas, Saurav Muralidharan, Ruisi Cai, Marcin Chochowski, Ameya Sunil Mahabaleshwarkar, Yoshi Suhara, Oluwatobi Olabiyi, Daniel Korzekwa, Mostofa Patwary, Mohammad Shoeybi, Jan Kautz, Bryan Catanzaro, Ashwath Aithal, Nima Tajbakhsh, Pavlo Molchanov

CoRR, November, 2025

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning.

Ali Taghibakhshi, Sharath Turuvekere Sreenivas, Saurav Muralidharan, Marcin Chochowski, Yashaswi Karnati, Raviraj Joshi, Ameya Sunil Mahabaleshwarkar, Zijia Chen, Yoshi Suhara, Oluwatobi Olabiyi, Daniel Korzekwa, Mostofa Patwary, Mohammad Shoeybi, Jan Kautz, Bryan Catanzaro, Ashwath Aithal, Nima Tajbakhsh, Pavlo Molchanov

CoRR, April, 2025

Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale.

Jerome Ku, Eric Nguyen, David W. Romero, Garyk Brixi, Brandon Yang, Anton Vorontsov, Ali Taghibakhshi, Amy X. Lu, Dave P. Burke, Greg Brockman, Stefano Massaroli, Christopher Ré, Patrick D. Hsu, Brian L. Hie, Stefano Ermon, Michael Poli

CoRR, March, 2025

Optimized Sparse Matrix Operations for Reverse Mode Automatic Differentiation.

Nicolas Nytko, Ali Taghibakhshi, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West

SIAM J. Sci. Comput., 2025

Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models.

Rohit Jena, Ali Taghibakhshi, Sahil Jain, Gerald Shen, Nima Tajbakhsh, Arash Vahdat

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025

2024

Generalizing reduction-based algebraic multigrid.

Tareq Uz Zaman, Nicolas Nytko, Ali Taghibakhshi, Scott P. MacLachlan, Luke N. Olson, Matthew West

Numer. Linear Algebra Appl., May, 2024

Generalizing Lloyd's Algorithm for Graph Clustering.

Tareq Uz Zaman, Nicolas Nytko, Ali Taghibakhshi, Scott P. MacLachlan, Luke N. Olson, Matthew West

SIAM J. Sci. Comput., 2024

NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment.

Gerald Shen, Zhilin Wang, Olivier Delalleau, Jiaqi Zeng, Yi Dong, Daniel Egert, Shengyang Sun, Jimmy J. Zhang, Sahil Jain, Ali Taghibakhshi, Markel Sanz Ausin, Ashwath Aithal, Oleksii Kuchaiev

CoRR, 2024

2023

MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods.

Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West

Proceedings of the International Conference on Machine Learning, 2023

Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching.

Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West

Proceedings of the Big Data Analytics and Knowledge Discovery, 2023

2022

Learning Interface Conditions in Domain Decomposition Solvers.

Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning.

Ali Taghibakhshi, Scott P. MacLachlan, Luke N. Olson, Matthew West

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Local Navigation and Docking of an Autonomous Robot Mower Using Reinforcement Learning and Computer Vision.

Ali Taghibakhshi, Nathan Ogden, Matthew West

Proceedings of the 13th International Conference on Computer and Automation Engineering, 2021