… deep learning methods can play a crucial role in solving both the aforementioned goals of
coding theory. While the learning … In part due to these challenges, recent deep learning works …

… these algorithms using experimental data that is collected by a chemical communication …
We show that deep learning algorithms perform significantly better than a simple detector …

… communication channels allow one to sample an unlimited amount of data to train and test
the communication algorithms … Motivated by the successes of deep learning in mathematically …

… learning is witnessing its golden era as deep learning slowly becomes the leader in this
domain. Deep learning uses … Some key enabler deep learning algorithms such as generative …

… Since reliable transmission is one of the most important tasks in wireless communications,
we will introduce DL-based transmission algorithm design in this section. Following the order …

… to train deep models with communication-efficient algorithms in Section 9. … Learning: A
Comprehensive Survey … In conclusion, while efficient communication algorithms reduce the …

… its overwhelming privilege in many areas, such as computer vision, robotics, and natural
language processing, due to its advanced algorithms and tools in learning complicated models. …

… In this work, we co-design an all-reduce communication algorithm and interconnection
architecture to support efficient and scalable all-reduce operation. We propose MULTITREE, a …

… blocks to adapt to emerging complex communication scenarios or outperform conventional
communication algorithms. This section presents some examples of DL applications that …

… To solve a practical problem with real-time constraints, we conventionally would first need
to design an algorithm and then develop a dedicated circuit based on it, which can be very …