data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing...
Computational methods for analyzing networks have typically been designed for static networks, which cannot capture the time-varying nature of many complex phenomena.In this dissertation, I propose new computational methods for machine learning and statistical inference on dynamic networks with time-...
Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging...
. Thus, we focused on language processing in the left hemisphere, but we also present the encoding results for the right hemisphere in Extended Data Fig.3. The raw signal was preprocessed to reflect high-frequency broadband (70–200 Hz) power activity (for full preprocessing procedures, see...
Part of the book series:Lecture Notes on Numerical Methods in Engineering and Sciences((LNNMES)) 1269Accesses Abstract Since the deep learning is now a hot topic in computational mechanics with neural networks and many related studies have been reported recently, we discuss here some features of ...
efficiency. For example, how parallel the system setup is; what architecture model has(e.g. group convolution costs in MACs); what computing platform the model uses(e.g. Cudnn has GPU acceleration for deep neural networks and standard operations such as forward or normalization are highly ...
we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experi...
A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning is a novel approach recently developed for modeling PDE solutions and shows promise to solve computational mechanics problems without using any labeled data....
《预订 Deep Learning for Computational Imaging: 9780198947172》,作者:预订 Deep Learning for Computational Imaging: 9780198947172Heckel 著,出版社:Oxford University Press,ISBN:9780198947172。
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning...