Zobrazeno 1 - 10
of 1 581
pro vyhledávání: '"Sanghavi P"'
Autor:
Wang, Haochen, Masui, Kiyoshi, Bandura, Kevin, Chakraborty, Arnab, Dobbs, Matt, Foreman, Simon, Gray, Liam, Halpern, Mark, Joseph, Albin, MacEachern, Joshua, Mena-Parra, Juan, Miller, Kyle, Newburgh, Laura, Paul, Sourabh, Reda, Alex, Sanghavi, Pranav, Siegel, Seth, Wulf, Dallas
The main challenge of 21 cm cosmology experiments is astrophysical foregrounds which are difficult to separate from the signal due to telescope systematics. An earlier study has shown that foreground residuals induced by antenna gain errors can be es
Externí odkaz:
http://arxiv.org/abs/2408.08949
Autor:
Sanghavi Parang, Jankharia Bhavin
Publikováno v:
Indian Journal of Radiology and Imaging, Vol 31, Iss 02, Pp 318-322 (2021)
Aim The aim of the study is to assess the effectiveness of low dose CT scan (LDCT) to pick up nodules and carcinomas in smokers in India. Methods A retrospective study of 350 smokers scanned with LDCT was performed in a private practice center in Mum
Externí odkaz:
https://doaj.org/article/c8663c75e65b4cccb8896789cad61e2c
Autor:
Tyndall, Will, Reda, Alex, Shaw, J. Richard, Bandura, Kevin, Chakraborty, Arnab, Kuhn, Emily, MacEachern, Joshua, Mena-Parra, Juan, Newburgh, Laura, Ordog, Anna, Pinsonneault-Marotte, Tristan, Polish, Anna Rose, Saliwanchik, Ben, Sanghavi, Pranav, Siegel, Seth R., Whitmer, Audrey, Wulf, Dallas
We present beam measurements of the CHIME telescope using a radio calibration source deployed on a drone payload. During test flights, the pulsing calibration source and the telescope were synchronized to GPS time, enabling in-situ background subtrac
Externí odkaz:
http://arxiv.org/abs/2407.04848
Autor:
Low, Yen Sia, Jackson, Michael L., Hyde, Rebecca J., Brown, Robert E., Sanghavi, Neil M., Baldwin, Julian D., Pike, C. William, Muralidharan, Jananee, Hui, Gavin, Alexander, Natasha, Hassan, Hadeel, Nene, Rahul V., Pike, Morgan, Pokrzywa, Courtney J., Vedak, Shivam, Yan, Adam Paul, Yao, Dong-han, Zipursky, Amy R., Dinh, Christina, Ballentine, Philip, Derieg, Dan C., Polony, Vladimir, Chawdry, Rehan N., Davies, Jordan, Hyde, Brigham B., Shah, Nigam H., Gombar, Saurabh
Evidence to guide healthcare decisions is often limited by a lack of relevant and trustworthy literature as well as difficulty in contextualizing existing research for a specific patient. Large language models (LLMs) could potentially address both ch
Externí odkaz:
http://arxiv.org/abs/2407.00541
Data pruning, the combinatorial task of selecting a small and informative subset from a large dataset, is crucial for mitigating the enormous computational costs associated with training data-hungry modern deep learning models at scale. Since large-s
Externí odkaz:
http://arxiv.org/abs/2406.17188
Autor:
Das, Rudrajit, Dhillon, Inderjit S., Epasto, Alessandro, Javanmard, Adel, Mao, Jieming, Mirrokni, Vahab, Sanghavi, Sujay, Zhong, Peilin
The performance of a model trained with \textit{noisy labels} is often improved by simply \textit{retraining} the model with its own predicted \textit{hard} labels (i.e., $1$/$0$ labels). Yet, a detailed theoretical characterization of this phenomeno
Externí odkaz:
http://arxiv.org/abs/2406.11206
Autor:
Li, Jeffrey, Fang, Alex, Smyrnis, Georgios, Ivgi, Maor, Jordan, Matt, Gadre, Samir, Bansal, Hritik, Guha, Etash, Keh, Sedrick, Arora, Kushal, Garg, Saurabh, Xin, Rui, Muennighoff, Niklas, Heckel, Reinhard, Mercat, Jean, Chen, Mayee, Gururangan, Suchin, Wortsman, Mitchell, Albalak, Alon, Bitton, Yonatan, Nezhurina, Marianna, Abbas, Amro, Hsieh, Cheng-Yu, Ghosh, Dhruba, Gardner, Josh, Kilian, Maciej, Zhang, Hanlin, Shao, Rulin, Pratt, Sarah, Sanyal, Sunny, Ilharco, Gabriel, Daras, Giannis, Marathe, Kalyani, Gokaslan, Aaron, Zhang, Jieyu, Chandu, Khyathi, Nguyen, Thao, Vasiljevic, Igor, Kakade, Sham, Song, Shuran, Sanghavi, Sujay, Faghri, Fartash, Oh, Sewoong, Zettlemoyer, Luke, Lo, Kyle, El-Nouby, Alaaeldin, Pouransari, Hadi, Toshev, Alexander, Wang, Stephanie, Groeneveld, Dirk, Soldaini, Luca, Koh, Pang Wei, Jitsev, Jenia, Kollar, Thomas, Dimakis, Alexandros G., Carmon, Yair, Dave, Achal, Schmidt, Ludwig, Shankar, Vaishaal
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretrai
Externí odkaz:
http://arxiv.org/abs/2406.11794
We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algorithms feature a simple update rule that requires solving only one lin
Externí odkaz:
http://arxiv.org/abs/2406.02016
Autor:
Lingam, Vijay, Tejaswi, Atula, Vavre, Aditya, Shetty, Aneesh, Gudur, Gautham Krishna, Ghosh, Joydeep, Dimakis, Alex, Choi, Eunsol, Bojchevski, Aleksandar, Sanghavi, Sujay
Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(\Delta W\). These \(\Delta W\) matrices are structured for efficient parameterization, often
Externí odkaz:
http://arxiv.org/abs/2405.19597
We explore exact generalized symmetries in the standard 2+1d lattice $\mathbb{Z}_2$ gauge theory coupled to the Ising model, and compare them with their continuum field theory counterparts. One model has a (non-anomalous) non-invertible symmetry, and
Externí odkaz:
http://arxiv.org/abs/2405.13105