Zobrazeno 1 - 10
of 2 521
pro vyhledávání: '"DO, MINH"'
Autor:
Rojas-Gomez, Renan A., Do, Minh N.
State-of-the-art Style Transfer methods often leverage pre-trained encoders optimized for discriminative tasks, which may not be ideal for image synthesis. This can result in significant artifacts and loss of photorealism. Motivated by the ability of
Externí odkaz:
http://arxiv.org/abs/2412.02214
Test-time adaptation (TTA) has emerged as a promising solution to tackle the continual domain shift in machine learning by allowing model parameters to change at test time, via self-supervised learning on unlabeled testing data. At the same time, it
Externí odkaz:
http://arxiv.org/abs/2412.01154
Autor:
Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, Nagpurkar, Priya
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co
Externí odkaz:
http://arxiv.org/abs/2411.13239
Strong solution and approximation of time-dependent radial Dunkl processes with multiplicative noise
We study the strong existence and uniqueness of solutions within a Weyl chamber for a class of time-dependent particle systems driven by multiplicative noise. This class includes well-known processes in physics and mathematical finance. We propose a
Externí odkaz:
http://arxiv.org/abs/2410.10457
Deep learning methods - consisting of a class of deep neural networks (DNNs) trained by a stochastic gradient descent (SGD) optimization method - are nowadays key tools to solve data driven supervised learning problems. Despite the great success of S
Externí odkaz:
http://arxiv.org/abs/2410.10533
Fairness in artificial intelligence and machine learning (AI/ML) models is becoming critically important, especially as decisions made by these systems impact diverse groups. In education, a vital sector for all countries, the widespread application
Externí odkaz:
http://arxiv.org/abs/2410.06423
In order to unlock the potential of diverse sensors, we investigate a method to transfer knowledge between modalities using the structure of a unified multimodal representation space for Human Action Recognition (HAR). We formalize and explore an und
Externí odkaz:
http://arxiv.org/abs/2407.16803
Autor:
Kamboj, Abhi, Do, Minh
Despite living in a multi-sensory world, most AI models are limited to textual and visual understanding of human motion and behavior. In fact, full situational awareness of human motion could best be understood through a combination of sensors. In th
Externí odkaz:
http://arxiv.org/abs/2403.15444
Autor:
Do, Minh T., Pless, Barry
Publikováno v:
Promotion de la santé et prévention des maladies chroniques au Canada, Vol 41, Iss 2, Pp 39-40 (2021)
Externí odkaz:
https://doaj.org/article/d287dab7e4ab470c9197e98c962ef57c
Autor:
Do, Minh T., Pless, Barry
Publikováno v:
Health Promotion and Chronic Disease Prevention in Canada, Vol 41, Iss 2, Pp 37-38 (2021)
Externí odkaz:
https://doaj.org/article/fb5a94c16e404c6294a64c330d02ba39