Zobrazeno 1 - 10
of 4 637
pro vyhledávání: '"A., Sathiya"'
Autor:
Wang, Ruofan, Prabhakar, Prakruthi, Srivastava, Gaurav, Wang, Tianqi, Jalali, Zeinab S., Bharill, Varun, Ouyang, Yunbo, Nigam, Aastha, Venugopalan, Divya, Gupta, Aman, Borisyuk, Fedor, Keerthi, Sathiya, Muralidharan, Ajith
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model up
Externí odkaz:
http://arxiv.org/abs/2403.00803
Autor:
Borisyuk, Fedor, Zhou, Mingzhou, Song, Qingquan, Zhu, Siyu, Tiwana, Birjodh, Parameswaran, Ganesh, Dangi, Siddharth, Hertel, Lars, Xiao, Qiang, Hou, Xiaochen, Ouyang, Yunbo, Gupta, Aman, Singh, Sheallika, Liu, Dan, Cheng, Hailing, Le, Lei, Hung, Jonathan, Keerthi, Sathiya, Wang, Ruoyan, Zhang, Fengyu, Kothari, Mohit, Zhu, Chen, Sun, Daqi, Dai, Yun, Luan, Xun, Zhu, Sirou, Wang, Zhiwei, Daftary, Neil, Shen, Qianqi, Jiang, Chengming, Wei, Haichao, Varshney, Maneesh, Ghoting, Amol, Ghosh, Souvik
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attention and resid
Externí odkaz:
http://arxiv.org/abs/2402.06859
Stochastic Gradient Descent (SGD) stands as a cornerstone optimization algorithm with proven real-world empirical successes but relatively limited theoretical understanding. Recent research has illuminated a key factor contributing to its practical e
Externí odkaz:
http://arxiv.org/abs/2401.12332
Autor:
Liu, Zirui, Song, Qingquan, Xiao, Qiang Charles, Selvaraj, Sathiya Keerthi, Mazumder, Rahul, Gupta, Aman, Hu, Xia
The large number of parameters in Pretrained Language Models enhance their performance, but also make them resource-intensive, making it challenging to deploy them on commodity hardware like a single GPU. Due to the memory and power limitations of th
Externí odkaz:
http://arxiv.org/abs/2401.04044
Publikováno v:
Journal of Clinical and Diagnostic Research, Vol 18, Iss 11, Pp 01-06 (2024)
Introduction: Transgender refers to an individual whose gender identity differs from the sex that was assigned at birth. Transgender women (MtF) were assigned male at birth but later recognised themselves as female. The length of the index finger (2D
Externí odkaz:
https://doaj.org/article/32bf5dc96ea445a9a52026b4318a1958
Autor:
N. Sathiya Narayanan, D. Sai Venkat Mohan, Javvadi Abhinay, Torlapati Dinesh, Veerla Satya Sai Surya Teja, Rajanala Praneeth
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-27 (2024)
Abstract The work evaluated the usage of various filler materials, namely aluminium oxide (Al2O3), magnesium (Mg), and glass powder, in the bidirectional glass fibre reinforced polymer (GFRP) composites. The required samples were fabricated using the
Externí odkaz:
https://doaj.org/article/4a8318e2124847cb9d8d4355b893ee5e
Publikováno v:
AIMS Mathematics, Vol 9, Iss 10, Pp 28741-28764 (2024)
This paper explores a fractional integro-differential equation with boundary conditions that incorporate the Hilfer-Hadamard fractional derivative. We model the RLC circuit using fractional calculus and define weighted spaces of continuous functions.
Externí odkaz:
https://doaj.org/article/6139d0c54a06434f966785bf2e106c45
Autor:
Behdin, Kayhan, Acharya, Ayan, Gupta, Aman, Song, Qingquan, Zhu, Siyu, Keerthi, Sathiya, Mazumder, Rahul
With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs. Drawing from recen
Externí odkaz:
http://arxiv.org/abs/2309.01885
Autor:
Mani, Sathiya Kumaran, Zhou, Yajie, Hsieh, Kevin, Segarra, Santiago, Chandra, Ranveer, Kandula, Srikanth
Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and inefficiencies. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2308.06261
Deep Learning based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classifying medical images, segmentation and identification. The existing architectures such as UN
Externí odkaz:
http://arxiv.org/abs/2305.07850