Zobrazeno 1 - 10
of 3 108
pro vyhledávání: '"Partovi A"'
Safety Verification for Evasive Collision Avoidance in Autonomous Vehicles with Enhanced Resolutions
Autor:
Arab, Aliasghar, Khaleghi, Milad, Partovi, Alireza, Abbaspour, Alireza, Shinde, Chaitanya, Mousavi, Yashar, Azimi, Vahid, Karimmoddini, Ali
This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is engineered to enhance collision avoidance and mitigat
Externí odkaz:
http://arxiv.org/abs/2411.02706
Autor:
Edalati, Ali, Ghaffari, Alireza, Asgharian, Masoud, Hou, Lu, Chen, Boxing, Nia, Vahid Partovi
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy required for their inference. Post-training Quantization (PTQ) techni
Externí odkaz:
http://arxiv.org/abs/2405.15025
The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Quantization (PTQ) often require calibration to achieve the desired accu
Externí odkaz:
http://arxiv.org/abs/2405.13358
In neural network binarization, BinaryConnect (BC) and its variants are considered the standard. These methods apply the sign function in their forward pass and their respective gradients are backpropagated to update the weights. However, the derivat
Externí odkaz:
http://arxiv.org/abs/2402.17710
Autor:
Ghaffari, Alireza, Yu, Justin, Nejad, Mahsa Ghazvini, Asgharian, Masoud, Chen, Boxing, Nia, Vahid Partovi
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. However, this approach is susceptible to the existence of outlier values in
Externí odkaz:
http://arxiv.org/abs/2312.09211
Autor:
Nia, Vahid Partovi, Zhang, Guojun, Kobyzev, Ivan, Metel, Michael R., Li, Xinlin, Sun, Ke, Hemati, Sobhan, Asgharian, Masoud, Kong, Linglong, Liu, Wulong, Chen, Boxing
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computer
Externí odkaz:
http://arxiv.org/abs/2303.15464
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Nanofibers show promise for wound healing by facilitating active agent delivery, moisture retention, and tissue regeneration. However, selecting suitable dressings for diverse wound types and managing varying exudate levels remains challengi
Externí odkaz:
https://doaj.org/article/a40e4b88b81a40dcbe29f30563683ae7
Autor:
Lakhmiri, Dounia, Zolnouri, Mahdi, Nia, Vahid Partovi, Tribes, Christophe, Digabel, Sébastien Le
Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar performance. Standard automatic design tools such as \emph{reinforcement le
Externí odkaz:
http://arxiv.org/abs/2301.06641
Autor:
Cacciola, Matteo, Frangioni, Antonio, Asgharian, Masoud, Ghaffari, Alireza, Nia, Vahid Partovi
Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for training such models, where the computations are usually performed in sin
Externí odkaz:
http://arxiv.org/abs/2301.01651
Autor:
Li, Xinlin, Parazeres, Mariana, Oberman, Adam, Ghaffari, Alireza, Asgharian, Masoud, Nia, Vahid Partovi
With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices. There are established compression method such as quantization, pruning, and architecture
Externí odkaz:
http://arxiv.org/abs/2212.11803