Zobrazeno 1 - 10
of 171
pro vyhledávání: '"Kumar, A. P. Siva"'
Autor:
Munoz, Gary D. Lopez, Minnich, Amanda J., Lutz, Roman, Lundeen, Richard, Dheekonda, Raja Sekhar Rao, Chikanov, Nina, Jagdagdorj, Bolor-Erdene, Pouliot, Martin, Chawla, Shiven, Maxwell, Whitney, Bullwinkel, Blake, Pratt, Katherine, de Gruyter, Joris, Siska, Charlotte, Bryan, Pete, Westerhoff, Tori, Kawaguchi, Chang, Seifert, Christian, Kumar, Ram Shankar Siva, Zunger, Yonatan
Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our daily lives. The increase in computational power and data availability has led to a proliferation of both single- and multi-modal models. As the GenAI ecosystem matures, the nee
Externí odkaz:
http://arxiv.org/abs/2410.02828
Autor:
Haider, Emman, Perez-Becker, Daniel, Portet, Thomas, Madan, Piyush, Garg, Amit, Ashfaq, Atabak, Majercak, David, Wen, Wen, Kim, Dongwoo, Yang, Ziyi, Zhang, Jianwen, Sharma, Hiteshi, Bullwinkel, Blake, Pouliot, Martin, Minnich, Amanda, Chawla, Shiven, Herrera, Solianna, Warreth, Shahed, Engler, Maggie, Lopez, Gary, Chikanov, Nina, Dheekonda, Raja Sekhar Rao, Jagdagdorj, Bolor-Erdene, Lutz, Roman, Lundeen, Richard, Westerhoff, Tori, Bryan, Pete, Seifert, Christian, Kumar, Ram Shankar Siva, Berkley, Andrew, Kessler, Alex
Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to en
Externí odkaz:
http://arxiv.org/abs/2407.13833
Autor:
Zhang, Alice Qian, Shaw, Ryland, Anthis, Jacy Reese, Milton, Ashlee, Tseng, Emily, Suh, Jina, Ahmad, Lama, Kumar, Ram Shankar Siva, Posada, Julian, Shestakofsky, Benjamin, Roberts, Sarah T., Gray, Mary L.
Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how
Externí odkaz:
http://arxiv.org/abs/2407.07786
Autor:
Musser, Micah, Lohn, Andrew, Dempsey, James X., Spring, Jonathan, Kumar, Ram Shankar Siva, Leong, Brenda, Liaghati, Christina, Martinez, Cindy, Grant, Crystal D., Rohrer, Daniel, Frase, Heather, Elliott, Jonathan, Bansemer, John, Rodriguez, Mikel, Regan, Mitt, Chowdhury, Rumman, Hermanek, Stefan
In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship be
Externí odkaz:
http://arxiv.org/abs/2305.14553
The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are present in the datasets inherently because the labeling is subjective
Externí odkaz:
http://arxiv.org/abs/2305.01884
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation C
Externí odkaz:
http://arxiv.org/abs/2303.09785
Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve appreciable performance on in-the-wild data sets. In-the-wild data sets
Externí odkaz:
http://arxiv.org/abs/2207.09012
Attacks from adversarial machine learning (ML) have the potential to be used "for good": they can be used to run counter to the existing power structures within ML, creating breathing space for those who would otherwise be the targets of surveillance
Externí odkaz:
http://arxiv.org/abs/2107.10302
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks charact
Externí odkaz:
http://arxiv.org/abs/2012.02048
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, "What are the potential lega
Externí odkaz:
http://arxiv.org/abs/2006.16179