Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Das, Srinjoy"'
Autor:
Bai, Xue, Haque, Tasmiah, Mohan, Sumit, Cai, Yuliang, Jeong, Byungheon, Halasz, Adam, Das, Srinjoy
We propose a deep learning based novel prediction framework for enhanced bandwidth reduction in motion transfer enabled video applications such as video conferencing, virtual reality gaming and privacy preservation for patient health monitoring. To m
Externí odkaz:
http://arxiv.org/abs/2403.11337
The Center for Disease Control estimates that over 37 million US adults suffer from chronic kidney disease (CKD), yet 9 out of 10 of these individuals are unaware of their condition due to the absence of symptoms in the early stages. It has a signifi
Externí odkaz:
http://arxiv.org/abs/2403.00965
Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the Laser Additive Manufa
Externí odkaz:
http://arxiv.org/abs/2312.04063
Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive resource utilization, and leng
Externí odkaz:
http://arxiv.org/abs/2311.09591
Autor:
Khosravi, Hamed, Farhadpour, Sarah, Grandhi, Manikanta, Raihan, Ahmed Shoyeb, Das, Srinjoy, Ahmed, Imtiaz
A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks
Externí odkaz:
http://arxiv.org/abs/2311.09333
Autor:
Das, Srinjoy, Rauchwerger, Lawrence
Several methods exist today to accelerate Machine Learning(ML) or Deep-Learning(DL) model performance for training and inference. However, modern techniques that rely on various graph and operator parallelism methodologies rely on search space optimi
Externí odkaz:
http://arxiv.org/abs/2308.11192
The Model-free Prediction Principle has been successfully applied to general regression problems, as well as problems involving stationary and locally stationary time series. In this paper we demonstrate how Model-Free Prediction can be applied to ha
Externí odkaz:
http://arxiv.org/abs/2212.03079
Autor:
Mishra, Srishti, Das, Srinjoy
Leveraging civic data, divided into 3 categories spending, infrastructure and citizen feedback, can present a clear picture of the priorities, performance, and pain-points of a city. Data driven insights highlight the current issues faced by citizens
Externí odkaz:
http://arxiv.org/abs/2211.03126
Quantization and pruning are core techniques used to reduce the inference costs of deep neural networks. State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to onl
Externí odkaz:
http://arxiv.org/abs/2110.08271
We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others. Our UCB distance tuning (UCB-D
Externí odkaz:
http://arxiv.org/abs/2110.02690