Zobrazeno 1 - 10
of 2 958
pro vyhledávání: '"P. Kabra"'
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
Autor:
Qian, Shenbin, Sindhujan, Archchana, Kabra, Minnie, Kanojia, Diptesh, Orăsan, Constantin, Ranasinghe, Tharindu, Blain, Frédéric
Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results
Externí odkaz:
http://arxiv.org/abs/2410.03278
Autor:
Su, Zhe, Zhou, Xuhui, Rangreji, Sanketh, Kabra, Anubha, Mendelsohn, Julia, Brahman, Faeze, Sap, Maarten
To be safely and successfully deployed, LLMs must simultaneously satisfy truthfulness and utility goals. Yet, often these two goals compete (e.g., an AI agent assisting a used car salesman selling a car with flaws), partly due to ambiguous or mislead
Externí odkaz:
http://arxiv.org/abs/2409.09013
Autor:
Kabra, Anmol, Patel, Kumar Kshitij
We study stochastic optimization in the context of performative shifts, where the data distribution changes in response to the deployed model. We demonstrate that naive retraining can be provably suboptimal even for simple distribution shifts. The is
Externí odkaz:
http://arxiv.org/abs/2408.08499
Autor:
Beyer, Lucas, Steiner, Andreas, Pinto, André Susano, Kolesnikov, Alexander, Wang, Xiao, Salz, Daniel, Neumann, Maxim, Alabdulmohsin, Ibrahim, Tschannen, Michael, Bugliarello, Emanuele, Unterthiner, Thomas, Keysers, Daniel, Koppula, Skanda, Liu, Fangyu, Grycner, Adam, Gritsenko, Alexey, Houlsby, Neil, Kumar, Manoj, Rong, Keran, Eisenschlos, Julian, Kabra, Rishabh, Bauer, Matthias, Bošnjak, Matko, Chen, Xi, Minderer, Matthias, Voigtlaender, Paul, Bica, Ioana, Balazevic, Ivana, Puigcerver, Joan, Papalampidi, Pinelopi, Henaff, Olivier, Xiong, Xi, Soricut, Radu, Harmsen, Jeremiah, Zhai, Xiaohua
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong
Externí odkaz:
http://arxiv.org/abs/2407.07726
Autor:
Wu, Ziyi, Rubanova, Yulia, Kabra, Rishabh, Hudson, Drew A., Gilitschenski, Igor, Aytar, Yusuf, van Steenkiste, Sjoerd, Allen, Kelsey R., Kipf, Thomas
We address the problem of multi-object 3D pose control in image diffusion models. Instead of conditioning on a sequence of text tokens, we propose to use a set of per-object representations, Neural Assets, to control the 3D pose of individual objects
Externí odkaz:
http://arxiv.org/abs/2406.09292