Zobrazeno 1 - 10
of 10 981
pro vyhledávání: '"P Chaudhari"'
Publikováno v:
Journal of Horticultural Sciences, Vol 18, Iss 2 (2023)
Tuberose is one of the most imperative bulbous crops with white fragrant florets, used for loose as well as cut flowers. All the commercial varieties have white colour flower only which is limiting factor in its popularity and marketing. By using edi
Externí odkaz:
https://doaj.org/article/e11924f5bb9346169193bb184898e2d4
Autor:
Huo, Zepeng, Fries, Jason Alan, Lozano, Alejandro, Valanarasu, Jeya Maria Jose, Steinberg, Ethan, Blankemeier, Louis, Chaudhari, Akshay S., Langlotz, Curtis, Shah, Nigam H.
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for
Externí odkaz:
http://arxiv.org/abs/2411.09361
Autonomous robots collaboratively exploring an unknown environment is still an open problem. The problem has its roots in coordination among non-stationary agents, each with only a partial view of information. The problem is compounded when the multi
Externí odkaz:
http://arxiv.org/abs/2411.08400
Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primari
Externí odkaz:
http://arxiv.org/abs/2411.04097
Autor:
De Silva, Ashwin, Ramesh, Rahul, Yang, Rubing, Yu, Siyu, Vogelstein, Joshua T, Chaudhari, Pratik
In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably approximately correct (PAC) learning, largely ignores time. As a consequenc
Externí odkaz:
http://arxiv.org/abs/2411.00109
Large language models (LLMs) are susceptible to memorizing training data, raising concerns due to the potential extraction of sensitive information. Current methods to measure memorization rates of LLMs, primarily discoverable extraction (Carlini et
Externí odkaz:
http://arxiv.org/abs/2410.19482
Autor:
Yang, Ke, Liu, Yao, Chaudhary, Sapana, Fakoor, Rasool, Chaudhari, Pratik, Karypis, George, Rangwala, Huzefa
Autonomy via agents using large language models (LLMs) for personalized, standardized tasks boosts human efficiency. Automating web tasks (like booking hotels within a budget) is increasingly sought after. Fulfilling practical needs, the web agent al
Externí odkaz:
http://arxiv.org/abs/2410.13825
Autor:
He, Shizhe, Paschali, Magdalini, Ouyang, Jiahong, Masood, Adnan, Chaudhari, Akshay, Adeli, Ehsan
Publikováno v:
International Workshop on Machine Learning in Clinical Neuroimaging (MLCN) 2024
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream tasks. This approach is particularly valuable in leveraging the structural
Externí odkaz:
http://arxiv.org/abs/2410.12053
Robustness towards adversarial attacks is a vital property for classifiers in several applications such as autonomous driving, medical diagnosis, etc. Also, in such scenarios, where the cost of misclassification is very high, knowing when to abstain
Externí odkaz:
http://arxiv.org/abs/2410.10736
Autor:
R, Renjith Kumar, Geethika, B R, Verma, Nancy, Chaudhari, Vishnu, Dave, Janvi, Joshi, Hem Chandra, Thomas, Jinto
In this work, we report an innovative pump-probe based experimental set up, to study the melting, subsequent evaporation, plasma formation and redeposition in a thin film coated on a glass substrate under different ambient conditions and laser fluenc
Externí odkaz:
http://arxiv.org/abs/2410.07755