Keivan Alizadeh-Vahid

According to our database, Keivan Alizadeh-Vahid authored at least 19 papers between 2019 and 2025.

Collaborative distances:

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

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, June, 2025

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, Mehrdad Farajtabar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

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

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

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

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

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

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the NeurIPS Efficient Natural Language and Speech Processing Workshop, 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

Proceedings of the NeurIPS Efficient Natural Language and Speech Processing Workshop, 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

Proceedings of the NeurIPS Efficient Natural Language and Speech Processing Workshop, 2024

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

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

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, Karen Khatamifard, Minsik Cho, Carlo C. del Mundo, Mohammad Rastegari, Mehrdad Farajtabar

CoRR, 2023

2022

DKM: Differentiable k-Means Clustering Layer for Neural Network Compression.

Minsik Cho, Keivan Alizadeh-Vahid, Saurabh Adya, Mohammad Rastegari

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

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

Butterfly Transform: An Efficient FFT Based Neural Architecture Design.

Keivan Alizadeh-Vahid, Anish Prabhu, Ali Farhadi, Mohammad Rastegari

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Butterfly Transform: An Efficient FFT Based Neural Architecture Design.

Keivan Alizadeh, Ali Farhadi, Mohammad Rastegari

CoRR, 2019