Practical Equivariances via Relational Conditional Neural Processes

• URL: http://arxiv.org/abs/2306.10915v2
• Date: Sun, 5 Nov 2023 13:33:00 GMT
• Title: Practical Equivariances via Relational Conditional Neural Processes
• Authors: Daolang Huang, Manuel Haussmann, Ulpu Remes, ST John, Gr\'egoire Clart\'e, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi
• Abstract summary: Conditional Neural Processes (CNPs) are a class of Conditional meta models popular for combining efficiency of amortized quantification with reliable uncertainty. We propose Conditional Neural Processes (RCNPs) as an effective approach to incorporate equivariances into any neural process model. Our proposed method extends the applicability and impact of equivariant processes to higher dimensions.
• Score: 20.192899181958264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatio-temporal modeling, Bayesian Optimization and continuous control, inherently contain equivariances -- for example to translation -- which the model can exploit for maximal performance. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions. In this work, we propose Relational Conditional Neural Processes (RCNPs), an effective approach to incorporate equivariances into any neural process model. Our proposed method extends the applicability and impact of equivariant neural processes to higher dimensions. We empirically demonstrate the competitive performance of RCNPs on a large array of tasks naturally containing equivariances.

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