A case for new neural network smoothness constraints

• URL: http://arxiv.org/abs/2012.07969v2
• Date: Mon, 21 Dec 2020 21:32:28 GMT
• Title: A case for new neural network smoothness constraints
• Authors: Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
• Abstract summary: We show that model smoothness is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. We conclude that new advances in the field are hinging on finding ways to incorporate data, tasks and learning into our definitions of smoothness.
• Score: 34.373610792075205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. We explore current methods of imposing smoothness constraints and observe they lack the flexibility to adapt to new tasks, they don't account for data modalities, they interact with losses, architectures and optimization in ways not yet fully understood. We conclude that new advances in the field are hinging on finding ways to incorporate data, tasks and learning into our definitions of smoothness.

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