Zobrazeno 1 - 10
of 1 784
pro vyhledávání: '"A. jandial"'
Autor:
A. Nair, A. khadwal, A. jain, S. attri, S. pattnaik, S. Naseem, N. verma, A. jandial, D. lad, G. prakash, P. malhotra
Publikováno v:
HemaSphere, Vol 6, Pp 1788-1789 (2022)
Externí odkaz:
https://doaj.org/article/65e553577bca4e14a790955c135c4dab
Publikováno v:
HemaSphere, Vol 6, Pp 1907-1908 (2022)
Externí odkaz:
https://doaj.org/article/52090774af4a4b1ba999eb22003b616a
Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style
Externí odkaz:
http://arxiv.org/abs/2405.16330
Autor:
Furniturewala, Shaz, Jandial, Surgan, Java, Abhinav, Banerjee, Pragyan, Shahid, Simra, Bhatia, Sumit, Jaidka, Kokil
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs. In this study, we examine w
Externí odkaz:
http://arxiv.org/abs/2405.10431
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241
Autor:
Banerjee, Pragyan, Java, Abhinav, Jandial, Surgan, Shahid, Simra, Furniturewala, Shaz, Krishnamurthy, Balaji, Bhatia, Sumit
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks. Recent methods employ expensive retraining or attempt debiasing during i
Externí odkaz:
http://arxiv.org/abs/2311.05451
Autor:
Menta, Tarun Ram, Jandial, Surgan, Patil, Akash, KB, Vimal, Bachu, Saketh, Krishnamurthy, Balaji, Balasubramanian, Vineeth N., Agarwal, Chirag, Sarkar, Mausoom
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensi
Externí odkaz:
http://arxiv.org/abs/2301.06928
Autor:
Jandial, Surgan, Khasbage, Yash, Pal, Arghya, Balasubramanian, Vineeth N, Krishnamurthy, Balaji
Publikováno v:
ECCV 2022
The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work Nasty Teacher proposed
Externí odkaz:
http://arxiv.org/abs/2210.11728
Autor:
Java, Abhinav, Deshmukh, Shripad, Aggarwal, Milan, Jandial, Surgan, Sarkar, Mausoom, Krishnamurthy, Balaji
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly pr
Externí odkaz:
http://arxiv.org/abs/2209.06584
Autor:
Xianhui Ruan, Wei Yan, Minghui Cao, Ray Anthony M. Daza, Miranda Y. Fong, Kaifu Yang, Jun Wu, Xuxiang Liu, Melanie Palomares, Xiwei Wu, Arthur Li, Yuan Chen, Rahul Jandial, Nicholas C. Spitzer, Robert F. Hevner, Shizhen Emily Wang
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Breast cancer metastasis to the brain is a clinical challenge rising in prevalence. However, the underlying mechanisms, especially how cancer cells adapt a distant brain niche to facilitate colonization, remain poorly understood. A unique me
Externí odkaz:
https://doaj.org/article/5c970c2a746849fab76890be4746d396