Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Keerthi, 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:
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:
Behdin, Kayhan, Song, Qingquan, Gupta, Aman, Keerthi, Sathiya, Acharya, Ayan, Ocejo, Borja, Dexter, Gregory, Khanna, Rajiv, Durfee, David, Mazumder, Rahul
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique modifies the fundamental loss function that steers gradient descent m
Externí odkaz:
http://arxiv.org/abs/2302.09693
Autor:
Behdin, Kayhan, Song, Qingquan, Gupta, Aman, Durfee, David, Acharya, Ayan, Keerthi, Sathiya, Mazumder, Rahul
Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these systems is the modification of the loss function. Sharpness-Aware Minim
Externí odkaz:
http://arxiv.org/abs/2212.04343
Autor:
Doan, Khoa D., Manchanda, Saurav, Wang, Fengjiao, Keerthi, Sathiya, Bhowmik, Avradeep, Reddy, Chandan K.
For a given image generation problem, the intrinsic image manifold is often low dimensional. We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small
Externí odkaz:
http://arxiv.org/abs/2003.11774
Autor:
Badirli, Sarkhan, Liu, Xuanqing, Xing, Zhengming, Bhowmik, Avradeep, Doan, Khoa, Keerthi, Sathiya S.
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and l
Externí odkaz:
http://arxiv.org/abs/2002.07971
Autor:
Keerthi, Sathiya S, Shevade, Shirish
Publikováno v:
IndraStra Global.
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic functio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::8c85eec9b4b1a7ec2a2e40fe64dfad6c
https://igi.indrastra.com/items/show/187588
https://igi.indrastra.com/items/show/187588
Autor:
Khanduja, Shwetabh, Nair, Vinod, Sundararajan, S., Raul, Ameya, Shaj, Ajesh Babu, Keerthi, Sathiya
Publikováno v:
2015 IEEE International Conference on Data Mining Workshop (ICDMW); 1/1/2015, p1624-1627, 4p