… , we examine various deep learning methods through the lens of their relational inductive
biases, showing that existing methods often carry relational assumptions which are not always …

… Training CNNs involves backpropagation and gradient descent, where the model adapts its
… research, we know that this is not good enough. If deep learning is to play a significant role …

… In recent years, the rapid advancement of machine learning … Air flows into the rotor cage
through a dual mechanism: it is … thresholds of each layer through backpropagation. This process …

… -end learning of communications systems without a channel … An introduction to deep learning
for the physical layer[J]. IEEE … Backpropagation through the void: optimizing control variates …

… and deterioration, however, are not fully understood and are … are then briefly introduced as
a machine learning technique and … during backpropagation merely affect the deepest layers (…

… Completing this work would have been more difficult without the support and friendship …
• For the algorithms, the author of [9] reviewed models based on deep learning approach …

… model does not try to distinguish the difference between different kinds of brick or plasterboard.
Indeed not even deterministic models … to the standard backpropagation network. Also, the …

… machine learning methods such as artificial neural network (… modeling. In the present paper,
an ANN is employed to establish the SGS stress model for incompressible turbulent channel …

… 其中,一些研究提出用深 度神经网络(DNN, deep neural network)来代替 无线收发机系统的
物理层中的某一个模块,比如 … .62001483);湖南省科技创新计划 基金资助项目(No.2020RC2045) …

… channel is modeled as the Rice fading, which is more generalized than the Rayleigh one.
The auto-encoder (AE) is constructed by using the deep neural networks (… without the channel …