The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption
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This thesis consists of three applications of machine learning techniques to empirical asset pricing.In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...
Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...
Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...
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State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...
DNA is an excellent medium for efficient storage of information. Not only it offers a long-term and robust mechanism but also it is environmental friendly and has an unparalleled storage capacity, However, the basic elements in DNA are quaternary, and ther ...
Atherosclerosis is a complex disease altering vasculature morphology, and subsequently flow, with progressive plaque formation, mural disruption, and lumen occlusion. Determination of clinically-relevant plaque components-particularly calcium, lipid, and f ...
The present invention proposes a method for detecting anomalous or out-of-distribution images in a machine learning system (1) comprising a pre-training network with a first encoder, and an anomaly detection network with a second encoder. The system is fir ...
Learning-based image coding has shown promising results during recent years. Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear tra ...