Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Jain, Swayambhoo"'
Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in real-world applications. A benchmark's usefulness is determined by its ab
Externí odkaz:
http://arxiv.org/abs/2408.08808
Autor:
Prabhakar, Raghu, Sivaramakrishnan, Ram, Gandhi, Darshan, Du, Yun, Wang, Mingran, Song, Xiangyu, Zhang, Kejie, Gao, Tianren, Wang, Angela, Li, Karen, Sheng, Yongning, Brot, Joshua, Sokolov, Denis, Vivek, Apurv, Leung, Calvin, Sabnis, Arjun, Bai, Jiayu, Zhao, Tuowen, Gottscho, Mark, Jackson, David, Luttrell, Mark, Shah, Manish K., Chen, Edison, Liang, Kaizhao, Jain, Swayambhoo, Thakker, Urmish, Huang, Dawei, Jairath, Sumti, Brown, Kevin J., Olukotun, Kunle
Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging. The disproportionate i
Externí odkaz:
http://arxiv.org/abs/2405.07518
Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints. In this paper we propose a Data-Driven Low-rank
Externí odkaz:
http://arxiv.org/abs/2107.05787
Autor:
Rakesh, Vineeth, Jain, Swayambhoo
Obtaining labeled data for machine learning tasks can be prohibitively expensive. Active learning mitigates this issue by exploring the unlabeled data space and prioritizing the selection of data that can best improve the model performance. A common
Externí odkaz:
http://arxiv.org/abs/2104.00896
Several complex tasks that arise in organizations can be simplified by mapping them into a matrix completion problem. In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects (VF
Externí odkaz:
http://arxiv.org/abs/1905.12881
Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a randomized
Externí odkaz:
http://arxiv.org/abs/1904.02841
Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly assume that the clean samples from the target distribution are easily av
Externí odkaz:
http://arxiv.org/abs/1902.04664
Autor:
Elyaderani, Mojtaba Kadkhodaie, Jain, Swayambhoo, Druce, Jeffrey, Gonella, Stefano, Haupt, Jarvis
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} structure in a {known,} fixed dictionary. We consider signals generated ac
Externí odkaz:
http://arxiv.org/abs/1708.08826
In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with one of the factors being sparse. We present general theoretical error bounds for an estimate obtain
Externí odkaz:
http://arxiv.org/abs/1704.02534
A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individu
Externí odkaz:
http://arxiv.org/abs/1703.06065