Zobrazeno 1 - 10
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pro vyhledávání: '"A Rajaram"'
Autor:
Roof, Katie (AUTHOR), Tan, Gillian (AUTHOR)
Publikováno v:
Bloomberg.com. 11/23/2024, pN.PAG-N.PAG. 1p.
Autor:
Numan, Nels, Rajaram, Shwetha, Kumaravel, Balasaravanan Thoravi, Marquardt, Nicolai, Wilson, Andrew D.
There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the
Externí odkaz:
http://arxiv.org/abs/2409.13926
Publikováno v:
Uttar Pradesh Journal of Zoology; 2024, Vol. 45 Issue 15, p129-136, 8p
Co-optimizing placement with congestion is integral to achieving high-quality designs. This paper presents GOALPlace, a new learning-based general approach to improving placement congestion by controlling cell density. Our method efficiently learns f
Externí odkaz:
http://arxiv.org/abs/2407.04579
Aperture synthesis observations with full polarisation have long been used to study the magnetic fields of synchrotron emitting sources. Recently proposed closure invariants give us a powerful method for extracting information from measured visibilit
Externí odkaz:
http://arxiv.org/abs/2407.00583
Autor:
Jasti, Jay, Zhong, Hua, Panwar, Vandana, Jarmale, Vipul, Miyata, Jeffrey, Carrillo, Deyssy, Christie, Alana, Rakheja, Dinesh, Modrusan, Zora, Kadel III, Edward Ernest, Beig, Niha, Huseni, Mahrukh, Brugarolas, James, Kapur, Payal, Rajaram, Satwik
Predictive biomarkers of treatment response are lacking for metastatic clear cell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angiosc
Externí odkaz:
http://arxiv.org/abs/2405.18327
Autor:
Sahni, Ashok
Publikováno v:
Current Science, 2011 Apr 01. 100(7), 1089-1089.
Externí odkaz:
https://www.jstor.org/stable/24076531
Autor:
Husain, Zakir, Hussain, Zakir
Publikováno v:
Proceedings of the Indian History Congress, 2011 Jan 01. 72, 319-323.
Externí odkaz:
https://www.jstor.org/stable/44146724
Akademický článek
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Autor:
Shaham, Tamar Rott, Schwettmann, Sarah, Wang, Franklin, Rajaram, Achyuta, Hernandez, Evan, Andreas, Jacob, Torralba, Antonio
This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a pre-trained vision-lan
Externí odkaz:
http://arxiv.org/abs/2404.14394