… Two characteristics of deep learning render it promising when applied to molecules. First,
deep learning methods can … Second, deep learning can perform feature extraction (or feature …

… deep learning-based NOS scheme outperforms HDM and Polar code with CRC-aided list
decoding for … Nilsson, “Algorithm and implementation of the K-best sphere decoding for MIMO …

… well with its own powerful learning ability, it also faces … the deep learning method to reconstruct
3D objects from a single image. Second, we comprehensively review encoders, decoders …

… Deep learning algorithms were then applied to reconstruct the wavelength from upsampled
QD data. We noted that a highly accurate image classification was reported by using …

… Illustration of the architecture of the deep learning network used in this manuscript. The input
… The deep learning hyperparameters were tuned following an empirical approach, with the …

… In this paper, we propose a novel phase unwrapping method by using deep learning
based semantic segmentation algorithm, as shown in Fig. 1. In our method, we employ the …

… Deep-learning models have been applied to many … A limited number of machine or
deep-learning models have … to expanding the use of deep learning for crystal structure …

… We propose a new deep auto-encoder processing unorder point clouds data with lower …
We design a new deep learning-based method for sparse point clouds geometry compression. It …

… , we propose a “one-to-many” deep learning technique that can analyze non-sinusoidal fringe
… We show for the first time, to the best of our knowledge, a trained deep neural network can …

… to a number of powerful approaches, such as sphere decoding, soft MIMO detection, convex
… incorporating deep learning in the model-driven signal processing problems is called deep …