Title: Rethinking skip connection model as a learnable Markov chain. Abstract: Over the past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization, and proven performance.
Prior work has explained the effectiveness of the skip connection mechanism from different perspectives.
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way.
However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers prone to getting trapped in local optimal points.
In order to move towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain.
Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks. 
The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection.