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pro vyhledávání: '"Narasimhan, Sai Shankar"'
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
Autor:
Narasimhan, Sai Shankar, Agarwal, Shubhankar, Akcin, Oguzhan, Sanghavi, Sujay, Chinchali, Sandeep
Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winter freeze. Such real-world time series are often enriched with paired h
Externí odkaz:
http://arxiv.org/abs/2403.02682
Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation, or the cloud, that runs compute-intensive deep neural network percepti
Externí odkaz:
http://arxiv.org/abs/2302.09182
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Akademický článek
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