This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates about ways to combine theoretical and practical aspects of both approaches for future research.

翻译：本文探讨了深度学习中两种新兴范式——神经细胞自动机与深度均衡模型——之间的关联与差异，并训练了一个简单的深度均衡卷积模型以展示基于NCA与DEQ方法的内在相似性。最后，本文对如何结合两种方法的理论与实践方面进行了展望，以期为未来研究提供方向。