Zobrazeno 1 - 10
of 3 315
pro vyhledávání: '"Rajabzadeh A"'
Autor:
Dialameh, Maryam, Rajabzadeh, Hossein, Sadeghi-Goughari, Moslem, Sim, Jung Suk, Kwon, Hyock Ju
Efficiently managing papillary thyroid microcarcinoma (PTMC) while minimizing patient discomfort poses a significant clinical challenge. Radiofrequency ablation (RFA) offers a less invasive alternative to surgery and radiation therapy for PTMC treatm
Externí odkaz:
http://arxiv.org/abs/2410.18239
Autor:
Rajabzadeh, Hossein, Jafari, Aref, Sharma, Aman, Jami, Benyamin, Kwon, Hyock Ju, Ghodsi, Ali, Chen, Boxing, Rezagholizadeh, Mehdi
Large Language Models (LLMs), with their increasing depth and number of parameters, have demonstrated outstanding performance across a variety of natural language processing tasks. However, this growth in scale leads to increased computational demand
Externí odkaz:
http://arxiv.org/abs/2409.14595
This is the first part of a series of papers devoted to the study of linear cocycles over chaotic systems. In the present paper, we show that every $\mathrm{SL}(d,\mathbb{R})$ cocycle over a shift of finite type either admits a dominated splitting or
Externí odkaz:
http://arxiv.org/abs/2408.13921
Autor:
Rajabzadeh, Taha, Boulton-McKeehan, Alex, Bonkowsky, Sam, Schuster, David I., Safavi-Naeini, Amir H.
Engineering the Hamiltonian of a quantum system is fundamental to the design of quantum systems. Automating Hamiltonian design through gradient-based optimization can dramatically accelerate this process. However, computing the gradients of eigenvalu
Externí odkaz:
http://arxiv.org/abs/2408.12704
We investigate the quantum Zeno effect as a framework for designing and analyzing quantum algorithms for Hamiltonian simulation. We show that frequent projective measurements of an ancilla qubit register can be used to simulate quantum dynamics on a
Externí odkaz:
http://arxiv.org/abs/2405.13589
Autor:
Rajabzadeh, Hossein, Valipour, Mojtaba, Zhu, Tianshu, Tahaei, Marzieh, Kwon, Hyock Ju, Ghodsi, Ali, Chen, Boxing, Rezagholizadeh, Mehdi
Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named QLoRA, significantly alleviates this issue, finding the efficient LoRA
Externí odkaz:
http://arxiv.org/abs/2402.10462
We employ a tool-interacting divide-and-conquer strategy enabling large language models (LLMs) to answer complex multimodal multi-hop questions. In particular, we harness the power of large language models to divide a given multimodal multi-hop quest
Externí odkaz:
http://arxiv.org/abs/2309.08922
Autor:
Rajabzadeh, Hesam, Safaee, Pedram
We prove the positivity of the top Lyapunov exponent of the twisted (spectral) cocycle, associated with IETs, with respect to a family of natural invariant measures. The proof relies on relating the top exponent to limits of exponents along families
Externí odkaz:
http://arxiv.org/abs/2309.05175
Autor:
Valipour, Mojtaba, Rezagholizadeh, Mehdi, Rajabzadeh, Hossein, Kavehzadeh, Parsa, Tahaei, Marzieh, Chen, Boxing, Ghodsi, Ali
Deep neural networks (DNNs) must cater to a variety of users with different performance needs and budgets, leading to the costly practice of training, storing, and maintaining numerous user/task-specific models. There are solutions in the literature
Externí odkaz:
http://arxiv.org/abs/2309.00255
Autor:
Abbasian, Mahyar, Rajabzadeh, Taha, Moradipari, Ahmadreza, Aqajari, Seyed Amir Hossein, Lu, Hongsheng, Rahmani, Amir
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over the generation process of GANs remains a significant hurdle. In this
Externí odkaz:
http://arxiv.org/abs/2307.13978