1 code implementation • 13 Mar 2024 • Yuexin Bian, Xiaohan Fu, Rajesh K. Gupta, Yuanyuan Shi
In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency.
1 code implementation • 28 Feb 2024 • Han Guo, Ramtin Hosseini, Ruiyi Zhang, Sai Ashish Somayajula, Ranak Roy Chowdhury, Rajesh K. Gupta, Pengtao Xie
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning.
1 code implementation • 2 Feb 2024 • Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta, Jingbo Shang
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
1 code implementation • 4 Oct 2023 • Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Earlence Fernandes
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities.
1 code implementation • AAAI Conference on Artificial Intelligence 2023 • Ranak Roy Chowdhury, Jiacheng Li, Xiyuan Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
no code implementations • 1 Jan 2023 • Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang
In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities.
1 code implementation • 1 Jan 2023 • Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set.
1 code implementation • 19 Jan 2021 • Judy P. Che-Castaldo, Rémi Cousin, Stefani Daryanto, Grace Deng, Mei-Ling E. Feng, Rajesh K. Gupta, Dezhi Hong, Ryan M. McGranaghan, Olukunle O. Owolabi, Tianyi Qu, Wei Ren, Toryn L. J. Schafer, Ashutosh Sharma, Chaopeng Shen, Mila Getmansky Sherman, Deborah A. Sunter, Lan Wang, David S. Matteson
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
no code implementations • 4 Sep 2019 • Francesco Fraternali, Bharathan Balaji, Yuvraj Agarwal, Rajesh K. Gupta
We propose using reinforcement learning to optimize the operation of energy harvesting sensors to maximize sensing quality with available energy.
1 code implementation • 10 Jun 2019 • Hamed Omidvar, Vahideh Akhlaghi, Massimo Franceschetti, Rajesh K. Gupta
We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space.
no code implementations • ICLR 2019 • Jeng-Hau Lin, Yunfan Yang, Rajesh K. Gupta, Zhuowen Tu
Memory and computation efficient deep learning architectures are crucial to the continued proliferation of machine learning capabilities to new platforms and systems.
no code implementations • 15 Jul 2017 • Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani Srivastava, Zhuowen Tu, Rajesh K. Gupta
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.