Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Gangopadhyay, Aryya"'
Quantum computing presents a double-edged sword: while it has the potential to revolutionize fields such as artificial intelligence, optimization, healthcare, and so on, it simultaneously poses a threat to current cryptographic systems, such as publi
Externí odkaz:
http://arxiv.org/abs/2410.13140
Autor:
Rashid, Hasib-Al, Sarkar, Argho, Gangopadhyay, Aryya, Rahnemoonfar, Maryam, Mohsenin, Tinoosh
Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged as a promising approach for running machine learning models on these de
Externí odkaz:
http://arxiv.org/abs/2404.03574
Federated Learning (FL) enables collaborative model building among a large number of participants without the need for explicit data sharing. But this approach shows vulnerabilities when privacy inference attacks are applied to it. In particular, in
Externí odkaz:
http://arxiv.org/abs/2210.13457
The availability of sensor-rich smart wearables and tiny, yet capable, unmanned vehicles such as nano quadcopters, opens up opportunities for a novel class of highly interactive, attention-shared human--machine teams. Reliable, lightweight, yet passi
Externí odkaz:
http://arxiv.org/abs/2208.05410
With the popularity of Internet of Things (IoT), edge computing and cloud computing, more and more stream analytics applications are being developed including real-time trend prediction and object detection on top of IoT sensing data. One popular typ
Externí odkaz:
http://arxiv.org/abs/2205.04622
TinyM$^2$Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently become very popular with the cl
Externí odkaz:
http://arxiv.org/abs/2202.04303
Autor:
Wang, Xin, Guo, Pei, Li, Xingyan, Gangopadhyay, Aryya, Busart, Carl E., Freeman, Jade, Wang, Jianwu
Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first
Externí odkaz:
http://arxiv.org/abs/2112.09762
Autor:
Menon, Sumeet, Mangalagiri, Jayalakshmi, Galita, Josh, Morris, Michael, Saboury, Babak, Yesha, Yaacov, Yesha, Yelena, Nguyen, Phuong, Gangopadhyay, Aryya, Chapman, David
We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem with a larger number of normal images. This algorithm is able to achieve
Externí odkaz:
http://arxiv.org/abs/2110.01605
Autor:
Mangalagiri, Jayalakshmi, Chapman, David, Gangopadhyay, Aryya, Yesha, Yaacov, Galita, Joshua, Menon, Sumeet, Yesha, Yelena, Saboury, Babak, Morris, Michael, Nguyen, Phuong
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D sca
Externí odkaz:
http://arxiv.org/abs/2104.02060
Autor:
Menon, Sumeet, Galita, Joshua, Chapman, David, Gangopadhyay, Aryya, Mangalagiri, Jayalakshmi, Nguyen, Phuong, Yesha, Yaacov, Yesha, Yelena, Saboury, Babak, Morris, Michael
COVID-19 is a novel infectious disease responsible for over 800K deaths worldwide as of August 2020. The need for rapid testing is a high priority and alternative testing strategies including X-ray image classification are a promising area of researc
Externí odkaz:
http://arxiv.org/abs/2009.12478