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Conditional Neural Processes
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #130

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet, GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.

Author Information

Marta Garnelo (DeepMind)
Dan Rosenbaum (DeepMind)
Chris Maddison (Oxford, DeepMind)
Tiago Ramalho (DeepMind)
David Saxton (DeepMind)
Murray Shanahan (DeepMind / Imperial College London)
Yee Teh (DeepMind)
Danilo J. Rezende (DeepMind)
Danilo J. Rezende

Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.

S. M. Ali Eslami (DeepMind)
S. M. Ali Eslami

S. M. Ali Eslami is a staff research scientist at DeepMind working on problems related to artificial intelligence. Prior to that, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his PhD in the School of Informatics at the University of Edinburgh, during which he was also a visiting researcher in the Visual Geometry Group at the University of Oxford. His research is focused on figuring out how we can get computers to learn with less human supervision.

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