We propose a non-representationalist framework for deep learning relying on a
novel method: computational phenomenology, a dialogue between the first-person
perspective (relying on phenomenology) and the mechanisms of computational
models. We thereby reject the modern cognitivist interpretation of deep
learning, according to which artificial neural networks encode representations
of external entities. This interpretation mainly relies on
neuro-representationalism, a position that combines a strong ontological
commitment towards scientific theoretical entities and the idea that the brain
operates on symbolic representations of these entities. We proceed as follows:
after offering a review of cognitivism and neuro-representationalism in the
field of deep learning, we first elaborate a phenomenological critique of these
positions; we then sketch out computational phenomenology and distinguish it
from existing alternatives; finally we apply this new method to deep learning
models trained on specific tasks, in order to formulate a conceptual framework
of deep-learning, that allows one to think of artificial neural networks'
mechanisms in terms of lived experience.

提出了一种基于计算现象学的深度学习非再现主义框架，该方法通过第一人称视角（依靠现象学）和计算模型机制之间的对话来实现。我们退回现代认知主义对深度学习的解释，即人工神经网络编码外部实体的表征。最终将该方法应用于特定任务的深度学习模型，以制定深度学习的概念框架，并允许将人工神经网络的机制视为生活体验。