Zobrazeno 1 - 10
of 41 270
pro vyhledávání: '"P Shankar"'
Autor:
Shankar, Manjari, Hartner, Anna-Maria, Arnold, Callum R. K., Gayawan, Ezra, Kang, Hyolim, Kim, Jong-Hoon, Gilani, Gemma Nedjati, Cori, Anne, Fu, Han, Jit, Mark, Muloiwa, Rudzani, Portnoy, Allison, Trotter, Caroline, Gaythorpe, Katy A. M.
Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and fina
Externí odkaz:
http://arxiv.org/abs/2410.13923
Autor:
Sampat, Shailaja Keyur, Nakamura, Mutsumi, Kailas, Shankar, Aggarwal, Kartik, Zhou, Mandy, Yang, Yezhou, Baral, Chitta
Deriving inference from heterogeneous inputs (such as images, text, and audio) is an important skill for humans to perform day-to-day tasks. A similar ability is desirable for the development of advanced Artificial Intelligence (AI) systems. While st
Externí odkaz:
http://arxiv.org/abs/2410.13666
Autor:
Chattopadhyay, Sameep, Paliwal, Pulkit, Narasimhan, Sai Shankar, Agarwal, Shubhankar, Chinchali, Sandeep P.
Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and polic
Externí odkaz:
http://arxiv.org/abs/2410.12672
Autor:
Narasimhan, Sai Shankar, Agarwal, Shubhankar, Rout, Litu, Shakkottai, Sanjay, Chinchali, Sandeep P.
Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications, these samples must meet certain hard constraints that are domain-speci
Externí odkaz:
http://arxiv.org/abs/2410.12652
Analyzing unstructured data, such as complex documents, has been a persistent challenge in data processing. Large Language Models (LLMs) have shown promise in this regard, leading to recent proposals for declarative frameworks for LLM-powered unstruc
Externí odkaz:
http://arxiv.org/abs/2410.12189
Autor:
Chen, Zixuan, He, Xialin, Wang, Yen-Jen, Liao, Qiayuan, Ze, Yanjie, Li, Zhongyu, Sastry, S. Shankar, Wu, Jiajun, Sreenath, Koushil, Gupta, Saurabh, Peng, Xue Bin
Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots. To facilitate successful deployment in the real world, smoothing techniques, such as low-pass filters and sm
Externí odkaz:
http://arxiv.org/abs/2410.11825
The increasing reliance on diffusion models for generating synthetic images has amplified concerns about the unauthorized use of personal data, particularly facial images, in model training. In this paper, we introduce a novel identity inference fram
Externí odkaz:
http://arxiv.org/abs/2410.10177
Autor:
Biswas, Mandas, Ray, Deb Shankar
We take a qualitative comparative look at quantum and classical quartic anharmonic oscillators. It has been shown that the behavior of the quantum anharmonic oscillator mimics that of the classical anharmonic oscillators with the structurally same Ha
Externí odkaz:
http://arxiv.org/abs/2410.09722
Recent studies have shown that chaotic maps are well-suited for applications requiring unpredictable behaviour, making them a valuable tool for enhancing unpredictability and complexity. A method is developed using 3D parametric equations to make bou
Externí odkaz:
http://arxiv.org/abs/2410.05215
Exploration of unknown, unstructured environments, such as in search and rescue, cave exploration, and planetary missions,presents significant challenges due to their unpredictable nature. This unpredictability can lead to inefficient path planning a
Externí odkaz:
http://arxiv.org/abs/2410.03917