Zobrazeno 1 - 10
of 21 184
pro vyhledávání: '"A. Rajaram"'
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
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:
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
To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts of human labor. We explore scalable methods for extracting the subgra
Externí odkaz:
http://arxiv.org/abs/2404.14349
Autor:
Mishra, Manit, Braham, Abderrahman, Marsom, Charles, Chung, Bryan, Griffin, Gavin, Sidnerlikar, Dakshesh, Sarin, Chatanya, Rajaram, Arjun
Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from
Externí odkaz:
http://arxiv.org/abs/2404.00188
BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI
Autor:
Rajaram, Shwetha, Numan, Nels, Kumaravel, Balasaravanan Thoravi, Marquardt, Nicolai, Wilson, Andrew D.
Today's video-conferencing tools support a rich range of professional and social activities, but their generic meeting environments cannot be dynamically adapted to align with distributed collaborators' needs. To enable end-user customization, we dev
Externí odkaz:
http://arxiv.org/abs/2403.13947
Publikováno v:
The 33rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC '24), June 3-7, 2024, Pisa, Italy. ACM, New York, NY, USA, 14 pages
Large language models are increasingly becoming a popular tool for software development. Their ability to model and generate source code has been demonstrated in a variety of contexts, including code completion, summarization, translation, and lookup
Externí odkaz:
http://arxiv.org/abs/2401.12554
This study introduces an innovative approach aimed at the efficient pruning of neural networks, with a particular focus on their deployment on edge devices. Our method involves the integration of the Lottery Ticket Hypothesis (LTH) with the Knowledge
Externí odkaz:
http://arxiv.org/abs/2401.10484