| Recurrent poisson factorization for temporal recommendation SA Hosseini, K Alizadeh, A Khodadadi, A Arabzadeh, M Farajtabar, H Zha, ... Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 72 | 2017 |
| Llm in a flash: Efficient large language model inference with limited memory K Alizadeh, I Mirzadeh, D Belenko, K Khatamifard, M Cho, CC Del Mundo, ... arXiv preprint arXiv:2312.11514, 2023 | 70 | 2023 |
| Butterfly Transform: An Efficient FFT Based Neural Architecture Design K Alizadeh-Vahid, A Prabhu, A Farhadi, M Rastegari Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 62* | 2020 |
| Relu strikes back: Exploiting activation sparsity in large language models I Mirzadeh, K Alizadeh, S Mehta, CC Del Mundo, O Tuzel, G Samei, ... arXiv preprint arXiv:2310.04564, 2023 | 53 | 2023 |
| Dkm: Differentiable k-means clustering layer for neural network compression M Cho, KA Vahid, S Adya, M Rastegari arXiv preprint arXiv:2108.12659, 2021 | 41 | 2021 |
| Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models I Mirzadeh, K Alizadeh, H Shahrokhi, O Tuzel, S Bengio, M Farajtabar arXiv preprint arXiv:2410.05229, 2024 | 27 | 2024 |
| Fluid: A unified evaluation framework for flexible sequential data M Wallingford, A Kusupati, K Alizadeh-Vahid, A Walsman, A Kembhavi, ... arXiv preprint arXiv:2007.02519, 2020 | 15* | 2020 |
| Apple intelligence foundation language models T Gunter, Z Wang, C Wang, R Pang, A Narayanan, A Zhang, B Zhang, ... arXiv preprint arXiv:2407.21075, 2024 | 14 | 2024 |
| eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models M Cho, KA Vahid, Q Fu, S Adya, CC Del Mundo, M Rastegari, D Naik, ... IEEE Computer Architecture Letters, 2024 | 4 | 2024 |
| Scaling smart: Accelerating large language model pre-training with small model initialization M Samragh, I Mirzadeh, KA Vahid, F Faghri, M Cho, M Nabi, D Naik, ... arXiv preprint arXiv:2409.12903, 2024 | 2 | 2024 |
| Computational Bottlenecks of Training Small-scale Large Language Models S Ashkboos, I Mirzadeh, K Alizadeh, MH Sekhavat, M Nabi, M Farajtabar, ... arXiv preprint arXiv:2410.19456, 2024 | 1 | 2024 |
| SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF A Chegini, H Kazemi, I Mirzadeh, D Yin, M Horton, M Nabi, M Farajtabar, ... arXiv preprint arXiv:2411.01798, 2024 | | 2024 |
| Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models K Alizadeh, I Mirzadeh, H Shahrokhi, D Belenko, F Sun, M Cho, ... arXiv preprint arXiv:2410.10846, 2024 | | 2024 |
| Butterfly transform layer A Farhadi, M Rastegari US Patent App. 16/892,192, 2020 | | 2020 |
| Model Soup for Better RLHF: Weight Space Averaging to Improve Alignment in LLMs A Chegini, H Kazemi, SI Mirzadeh, D Yin, M Horton, M Nabi, M Farajtabar, ... NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles …, 0 | | |
| 2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32 T Abeywickrama, TB Adji, I Agrafiotis, S Agrawal, NK Ahmed, R Akbarinia, ... | | |
Mehrdad FarajtabarResearch Scientist at AppleDirección de correo verificada de apple.com
