Aswathy Ravikumar, Harini Sriraman^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.034710

Abstract Deep neural networks are gaining importance and popularity in applications and services. Due to the enormous number of learnable parameters and datasets, the training of neural networks is computationally costly. Parallel and distributed computation-based strategies are used to accelerate this training process. Generative Adversarial Networks (GAN) are a recent technological achievement in deep learning. These generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous datasets. Typically, a GAN is trained on a single server. Conventional deep learning accelerator designs are challenged by the unique properties of GAN, like the enormous computation stages… More >

Yiming Huang^{1, *}, Hang Lei¹, Xiaoyu Li^{1, *}, Qingsheng Zhu², Wanghao Ren³, Xusheng Liu^{2, 4}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390

Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this… More >

Syed Falahuddin Quadri¹, Xiaoyu Li^1,*, Desheng Zheng², Muhammad Umar Aftab¹, Yiming Huang³

Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899

Abstract Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the… More >

Xintao Duan^1,*, Haoxian Song¹, Chuan Qin², Muhammad Khurram Khan³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 483-493, 2018, DOI: 10.3970/cmc.2018.01798

Abstract In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image.… More >