Zobrazeno 1 - 10
of 9 303
pro vyhledávání: '"Kabra, A. A."'
Software vulnerabilities are commonly exploited as attack vectors in cyberattacks. Hence, it is crucial to identify vulnerable software configurations early to apply preventive measures. Effective vulnerability detection relies on identifying softwar
Externí odkaz:
http://arxiv.org/abs/2412.16607
Autor:
Carreira, João, Gokay, Dilara, King, Michael, Zhang, Chuhan, Rocco, Ignacio, Mahendran, Aravindh, Keck, Thomas Albert, Heyward, Joseph, Koppula, Skanda, Pot, Etienne, Erdogan, Goker, Hasson, Yana, Yang, Yi, Greff, Klaus, Moing, Guillaume Le, van Steenkiste, Sjoerd, Zoran, Daniel, Hudson, Drew A., Vélez, Pedro, Polanía, Luisa, Friedman, Luke, Duvarney, Chris, Goroshin, Ross, Allen, Kelsey, Walker, Jacob, Kabra, Rishabh, Aboussouan, Eric, Sun, Jennifer, Kipf, Thomas, Doersch, Carl, Pătrăucean, Viorica, Damen, Dima, Luc, Pauline, Sajjadi, Mehdi S. M., Zisserman, Andrew
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x2013}$ action classification, ImageNet classification, etc. In this pape
Externí odkaz:
http://arxiv.org/abs/2412.15212
Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI
Autor:
Krsek, Isadora, Kabra, Anubha, Dou, Yao, Naous, Tarek, Dabbish, Laura A., Ritter, Alan, Xu, Wei, Das, Sauvik
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to tell that t
Externí odkaz:
http://arxiv.org/abs/2412.15047
Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases with other task-specific shortcomings, such as comprehension failure. For
Externí odkaz:
http://arxiv.org/abs/2412.11414
We introduce a hierarchical probabilistic approach to go from a 2D image to multiview 3D: a diffusion "prior" models the unseen 3D geometry, which then conditions a diffusion "decoder" to generate novel views of the subject. We use a pointmap-based g
Externí odkaz:
http://arxiv.org/abs/2412.10273
Autor:
Mangu, Aashrita, Westbrook, Benjamin, Beckman, Shawn, Corbett, Lance, Crowley, Kevin T., Dutcher, Daniel, Johnson, Bradley R., Lee, Adrian T., Kabra, Varun, Prasad, Bhoomija, Staggs, Suzanne T., Suzuki, Aritoki, Wang, Yuhan, Zheng, Kaiwen
Publikováno v:
J Low Temp Phys (2024)
The Simons Observatory (SO) is a cosmic microwave background (CMB) experiment located in the Atacama Desert in Chile that will make precise temperature and polarization measurements over six spectral bands ranging from 27 to 285 GHz. Three small aper
Externí odkaz:
http://arxiv.org/abs/2412.01204
Autor:
Mangu, Aashrita, Corbett, Lance, Bhimani, Sanah, Carl, Fred, Day-Weiss, Samuel, DiGia, Brooke, Errard, Josquin, Galitzki, Nicholas, Hazumi, Masashi, Henderson, Shawn W., Kabra, Varun, Miller, Amber, Moore, Jenna, Song, Xue, Tsan, Tran, Wang, Yuhan, Zonca, Andrea
Publikováno v:
PoS. TAUP2023 (2024) 003
The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment located in the Atacama Desert in Chile at an elevation of 5200 meters, nominally consisting of an array of three 0.42-meter small aperture telescopes (SATs) and one
Externí odkaz:
http://arxiv.org/abs/2412.01200
Autor:
van Steenkiste, Sjoerd, Zoran, Daniel, Yang, Yi, Rubanova, Yulia, Kabra, Rishabh, Doersch, Carl, Gokay, Dilara, Heyward, Joseph, Pot, Etienne, Greff, Klaus, Hudson, Drew A., Keck, Thomas Albert, Carreira, Joao, Dosovitskiy, Alexey, Sajjadi, Mehdi S. M., Kipf, Thomas
Current vision models typically maintain a fixed correspondence between their representation structure and image space. Each layer comprises a set of tokens arranged "on-the-grid," which biases patches or tokens to encode information at a specific sp
Externí odkaz:
http://arxiv.org/abs/2411.05927
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification
Autor:
Hogan, Brendan, Kabra, Anmol, Pacheco, Felipe Siqueira, Greenstreet, Laura, Fan, Joshua, Ferber, Aaron, Ummus, Marta, Brito, Alecsander, Graham, Olivia, Aoki, Lillian, Harvell, Drew, Flecker, Alex, Gomes, Carla
Trust and interpretability are crucial for the use of Artificial Intelligence (AI) in scientific research, but current models often operate as black boxes offering limited transparency and justifications for their outputs. We introduce AiSciVision, a
Externí odkaz:
http://arxiv.org/abs/2410.21480
We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-
Externí odkaz:
http://arxiv.org/abs/2410.06290