Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Aboutalebi, Hossein"'
Autor:
Aboutalebi, Hossein, Song, Hwanjun, Xie, Yusheng, Gupta, Arshit, Sun, Justin, Su, Hang, Shalyminov, Igor, Pappas, Nikolaos, Singh, Siffi, Mansour, Saab
Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous approaches augment textual dialogues with retrieved images, posing pr
Externí odkaz:
http://arxiv.org/abs/2403.03194
The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and vis
Externí odkaz:
http://arxiv.org/abs/2306.01272
Autor:
Pavlova, Maya, Tuinstra, Tia, Aboutalebi, Hossein, Zhao, Andy, Gunraj, Hayden, Wong, Alexander
After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing b
Externí odkaz:
http://arxiv.org/abs/2206.03671
Autor:
Aboutalebi, Hossein, Pavlova, Maya, Shafiee, Mohammad Javad, Florea, Adrian, Hryniowski, Andrew, Wong, Alexander
Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants.
Externí odkaz:
http://arxiv.org/abs/2204.11210
Autor:
Aboutalebi, Hossein, Pavlova, Maya, Gunraj, Hayden, Shafiee, Mohammad Javad, Sabri, Ali, Alaref, Amer, Wong, Alexander
Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this work, we explore the concept of self-attention for tackling such
Externí odkaz:
http://arxiv.org/abs/2110.06063
Autor:
Chung, Audrey G., Pavlova, Maya, Gunraj, Hayden, Terhljan, Naomi, MacLean, Alexander, Aboutalebi, Hossein, Surana, Siddharth, Zhao, Andy, Abbasi, Saad, Wong, Alexander
As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 clinical workflow. These machine learning methods are typically stand-alo
Externí odkaz:
http://arxiv.org/abs/2109.06421
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms
Externí odkaz:
http://arxiv.org/abs/2109.03975
Autor:
Aboutalebi, Hossein, Shafiee, Mohammad Javad, Karg, Michelle, Scharfenberger, Christian, Wong, Alexander
Despite the significant advances in deep learning over the past decade, a major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This sensitivity to making erroneous predictions in the p
Externí odkaz:
http://arxiv.org/abs/2106.10212
Autor:
Pavlova, Maya, Terhljan, Naomi, Chung, Audrey G., Zhao, Andy, Surana, Siddharth, Aboutalebi, Hossein, Gunraj, Hayden, Sabri, Ali, Alaref, Amer, Wong, Alexander
As the COVID-19 pandemic continues to devastate globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint. As part of the COVID-Ne
Externí odkaz:
http://arxiv.org/abs/2105.06640
The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the number of cases has put considerable strain on health care systems around
Externí odkaz:
http://arxiv.org/abs/2105.01284