Butterfly Transform: An Efficient FFT Based Neural Architecture Design. - Keivan Alizadeh; Ali Farhadi; Mohammad Rastegari (CoRR, 2019)

Butterfly Transform: An Efficient FFT Based Neural Architecture Design. - Keivan Alizadeh-Vahid; Anish Prabhu; Ali Farhadi; Mohammad Rastegari (CVPR, 2020)

In the Wild: From ML Models to Pragmatic ML Systems. - Matthew Wallingford; Aditya Kusupati; Keivan Alizadeh-Vahid; Aaron Walsman; Aniruddha Kembhavi; Ali Farhadi (CoRR, 2020)

Recurrent Poisson Factorization for Temporal Recommendation. - Seyed Abbas Hosseini; Ali Khodadadi; Keivan Alizadeh; Ali Arabzadeh; Mehrdad Farajtabar; Hongyuan Zha; Hamid R. Rabiee (IEEE Trans. Knowl. Data Eng., 2020)

DKM: Differentiable k-Means Clustering Layer for Neural Network Compression. - Minsik Cho; Keivan Alizadeh-Vahid; Saurabh Adya; Mohammad Rastegari (ICLR, 2022)

LLM in a flash: Efficient Large Language Model Inference with Limited Memory. - Keivan Alizadeh; Iman Mirzadeh; Dmitry Belenko; Karen Khatamifard; Minsik Cho; Carlo C. del Mundo; Mohammad Rastegari; Mehrdad Farajtabar (CoRR, 2023)

FLUID: A Unified Evaluation Framework for Flexible Sequential Data. - Matthew Wallingford; Aditya Kusupati; Keivan Alizadeh-Vahid; Aaron Walsman; Aniruddha Kembhavi; Ali Farhadi (Trans. Mach. Learn. Res., 2023)

LLM in a flash: Efficient Large Language Model Inference with Limited Memory. - Keivan Alizadeh; Iman Mirzadeh; Dmitry Belenko; S. Khatamifard; Minsik Cho; Carlo C. del Mundo; Mohammad Rastegari; Mehrdad Farajtabar (ACL (1), 2024)

Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models. - Keivan Alizadeh-Vahid; Seyed-Iman Mirzadeh; Hooman Shahrkokhi; Dmitry Belenko; Frank Sun; Minsik Cho; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar (ENLSP, 2024)

Computational Bottlenecks of Training Small-scale Large Language Models. - Saleh Ashkboos; Seyed Iman Mirzadeh; Keivan Alizadeh-Vahid; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar; Fartash Faghri (ENLSP, 2024)

Scaling Smart: Accelerating Large Language Model Pre-Training with Small Model Initialization. - Mohammad Samragh; Seyed-Iman Mirzadeh; Keivan Alizadeh-Vahid; Fartash Faghri; Minsik Cho; Moin Nabi; Devang Naik; Mehrdad Farajtabar (ENLSP, 2024)

ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models. - Iman Mirzadeh; Keivan Alizadeh-Vahid; Sachin Mehta; Carlo C. del Mundo; Oncel Tuzel; Golnoosh Samei; Mohammad Rastegari; Mehrdad Farajtabar (ICLR, 2024)

eDKM: An Efficient and Accurate Train-Time Weight Clustering for Large Language Models. - Minsik Cho; Keivan Alizadeh-Vahid; Qichen Fu; Saurabh Adya; Carlo C. del Mundo; Mohammad Rastegari; Devang Naik; Peter Zatloukal (IEEE Comput. Archit. Lett., 2024)

Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization. - Mohammad Samragh; Iman Mirzadeh; Keivan Alizadeh-Vahid; Fartash Faghri; Minsik Cho; Moin Nabi; Devang Naik; Mehrdad Farajtabar (CoRR, 2024)

Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models. - Keivan Alizadeh; Iman Mirzadeh; Hooman Shahrokhi; Dmitry Belenko; Frank Sun; Minsik Cho; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar (CoRR, 2024)

Computational Bottlenecks of Training Small-scale Large Language Models. - Saleh Ashkboos; Iman Mirzadeh; Keivan Alizadeh; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar; Fartash Faghri (CoRR, 2024)

SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF. - Atoosa Malemir Chegini; Hamid Kazemi; Iman Mirzadeh; Dong Yin; Maxwell Horton; Moin Nabi; Mehrdad Farajtabar; Keivan Alizadeh (CoRR, 2024)

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. - Iman Mirzadeh; Keivan Alizadeh; Hooman Shahrokhi; Oncel Tuzel; Samy Bengio; Mehrdad Farajtabar (ICLR, 2025)

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity. - Parshin Shojaee; Iman Mirzadeh; Keivan Alizadeh; Maxwell Horton; Samy Bengio; Mehrdad Farajtabar (CoRR, 2025)