Zobrazeno 1 - 10
of 4 058
pro vyhledávání: '"A. Mirzadeh"'
Autor:
M. Salehi, R. Mahdavi, M. Rezai, M. Ghorbani, A.R. Nafarzadegan, A. Mirzadeh, A. Mahdavi, A. Khoorani
Publikováno v:
Desert, Vol 28, Iss 2, Pp 291-316 (2023)
The current research aims to assess the feasibility of groundwater resources in two soft and hard formations of the Shamil-Takht basin using logic and operators Fuzzy and Boolean in the GIS environment. For this purpose, eight and seven thematic laye
Externí odkaz:
https://doaj.org/article/13d492f923824aa9b6849de72b5878a3
Autor:
Chegini, Atoosa, Kazemi, Hamid, Mirzadeh, Iman, Yin, Dong, Horton, Maxwell, Nabi, Moin, Farajtabar, Mehrdad, Alizadeh, Keivan
In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current polic
Externí odkaz:
http://arxiv.org/abs/2411.01798
Autor:
Ashkboos, Saleh, Mirzadeh, Iman, Alizadeh, Keivan, Sekhavat, Mohammad Hossein, Nabi, Moin, Farajtabar, Mehrdad, Faghri, Fartash
While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. However, there is limited research on the training behavior and computatio
Externí odkaz:
http://arxiv.org/abs/2410.19456
Autor:
Mirzadeh, Iman, Alizadeh, Keivan, Shahrokhi, Hooman, Tuzel, Oncel, Bengio, Samy, Farajtabar, Mehrdad
Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level que
Externí odkaz:
http://arxiv.org/abs/2410.05229
Autor:
Alizadeh, Keivan, Mirzadeh, Iman, Shahrokhi, Hooman, Belenko, Dmitry, Sun, Frank, Cho, Minsik, Sekhavat, Mohammad Hossein, Nabi, Moin, Farajtabar, Mehrdad
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding
Externí odkaz:
http://arxiv.org/abs/2410.10846
Autor:
Samragh, Mohammad, Mirzadeh, Iman, Vahid, Keivan Alizadeh, Faghri, Fartash, Cho, Minsik, Nabi, Moin, Naik, Devang, Farajtabar, Mehrdad
The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters can be extremely slow and costly. In contrast, small language models are
Externí odkaz:
http://arxiv.org/abs/2409.12903
Autor:
Mehta, Sachin, Sekhavat, Mohammad Hossein, Cao, Qingqing, Horton, Maxwell, Jin, Yanzi, Sun, Chenfan, Mirzadeh, Iman, Najibi, Mahyar, Belenko, Dmitry, Zatloukal, Peter, Rastegari, Mohammad
The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we releas
Externí odkaz:
http://arxiv.org/abs/2404.14619
Autor:
Alizadeh, Keivan, Mirzadeh, Iman, Belenko, Dmitry, Khatamifard, Karen, Cho, Minsik, Del Mundo, Carlo C, Rastegari, Mohammad, Farajtabar, Mehrdad
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limi
Externí odkaz:
http://arxiv.org/abs/2312.11514
Autor:
Mirzadeh, Iman, Alizadeh, Keivan, Mehta, Sachin, Del Mundo, Carlo C, Tuzel, Oncel, Samei, Golnoosh, Rastegari, Mohammad, Farajtabar, Mehrdad
Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices. Despite rec
Externí odkaz:
http://arxiv.org/abs/2310.04564
Autor:
Kleinbock, Dmitry, Mirzadeh, Shahriar
Let $X = G/\Gamma$, where $G$ is a Lie group and $\Gamma$ is a uniform lattice in $G$, and let $O$ be an open subset of $X$. We give an upper estimate for the Hausdorff dimension of the set of points whose trajectories escape $O$ on average with freq
Externí odkaz:
http://arxiv.org/abs/2310.00122