Zobrazeno 1 - 10
of 690
pro vyhledávání: '"Le Quoc P"'
Autor:
Nguyen Minh Ha, Le Quoc Phong, Tran Kiem Viet Thang, Huynh Luong Tam, Tran Hai Dang, Nguyen Thi Mai Hoa
Publikováno v:
Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, Vol 13, Iss 2, Pp 19-38 (2022)
This study builds on a strong theoretical background to investigate the relationship between Corporate Social Responsibility (CSR) and consumer repurchase intention through a corporate reputation for McDonald’s distribution channel in Viet Nam. The
Externí odkaz:
https://doaj.org/article/1ab8578d5f0a40b381292c0bc12b5f16
In this paper, we investigate the butterfly factorization problem, i.e., the problem of approximating a matrix by a product of sparse and structured factors. We propose a new formal mathematical description of such factors, that encompasses many diff
Externí odkaz:
http://arxiv.org/abs/2411.04506
Autor:
Ye, Ziyu, Agarwal, Rishabh, Liu, Tianqi, Joshi, Rishabh, Velury, Sarmishta, Le, Quoc V., Tan, Qijun, Liu, Yuan
Current RLHF frameworks for aligning large language models (LLMs) typically assume a fixed prompt distribution, which is sub-optimal and limits the scalability of alignment and generalizability of models. To address this, we introduce a general open-
Externí odkaz:
http://arxiv.org/abs/2411.00062
Despite their success in many domains, large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. This is crucial as many real-world applications, ranging from personalized recommendations to h
Externí odkaz:
http://arxiv.org/abs/2410.06238
Autor:
Vodrahalli, Kiran, Ontanon, Santiago, Tripuraneni, Nilesh, Xu, Kelvin, Jain, Sanil, Shivanna, Rakesh, Hui, Jeffrey, Dikkala, Nishanth, Kazemi, Mehran, Fatemi, Bahare, Anil, Rohan, Dyer, Ethan, Shakeri, Siamak, Vij, Roopali, Mehta, Harsh, Ramasesh, Vinay, Le, Quoc, Chi, Ed, Lu, Yifeng, Firat, Orhan, Lazaridou, Angeliki, Lespiau, Jean-Baptiste, Attaluri, Nithya, Olszewska, Kate
We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbit
Externí odkaz:
http://arxiv.org/abs/2409.12640
Publikováno v:
MAPR2024
Cardiovascular disease remains a predominant global health concern, responsible for a significant portion of mortality worldwide. Accurate segmentation of cardiac medical imaging data is pivotal in mitigating fatality rates associated with cardiovasc
Externí odkaz:
http://arxiv.org/abs/2409.05280
Autor:
Brown, Bradley, Juravsky, Jordan, Ehrlich, Ryan, Clark, Ronald, Le, Quoc V., Ré, Christopher, Mirhoseini, Azalia
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the amount of compute to only one attempt per problem. Here, we explore inference compute as
Externí odkaz:
http://arxiv.org/abs/2407.21787
Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal with not just textual but also visual inputs. Despite rapid progress
Externí odkaz:
http://arxiv.org/abs/2407.15680
We first show a simple but striking result in bilevel optimization: unconstrained $C^\infty$ smooth bilevel programming is as hard as general extended-real-valued lower semicontinuous minimization. We then proceed to a worst-case analysis of box-cons
Externí odkaz:
http://arxiv.org/abs/2407.12372
Autor:
Zheng, Huaixiu Steven, Mishra, Swaroop, Zhang, Hugh, Chen, Xinyun, Chen, Minmin, Nova, Azade, Hou, Le, Cheng, Heng-Tze, Le, Quoc V., Chi, Ed H., Zhou, Denny
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluation on the planning capabilities of LLMs with full information on the
Externí odkaz:
http://arxiv.org/abs/2406.04520