NEURAL PROCESSES

M GarneloJ SchwarzD RosenbaumF ViolaD J RezendeS M A EslamiY W Teh

We extend 'Conditional Neural Processes' to be able to generate different samples given the same context points. We achieve this by introducing a global latent variable that allows for consistency across samples. 

CONDITIONAL NEURAL PROCESSES

M GarneloD Rosenbaum, C J Maddison, T Ramalho, D Saxton, M Shanahan, Y W Teh, D J Rezende, S M A Eslami

We introduce neural processes, a generalisation of the GQN framework to a range of few-shot tasks like regression and classification. Neural processes are flexible, computationally efficient at test-time and learn to estimate uncertainty given some context points.

GENERATIVE QUERY NETWORKS

S M A Eslami, D J Rezende, F Besse, F Viola, A S Morcos, M Garnelo, A Ruderman, A A Rusu, I Danihelka, K Gregor, D P Reichert, L Buesing, T Weber, O Vinyals, D Rosenbaum, N Rabinowitz, H King, C Hillier, M Botvinick, D Wierstra, K Kavukcuoglu, D Hassabis

Generative query networks learn to predict what 3D scenes look like viewed from a new position given some context observations of that scene from other viewpoints. As part of my internship I used the representations learned by GQN to learn the reaching task with a simulated robot arm.

TOWARDS DEEP SYMBOLIC REINFORCEMENT LEARNING

M Garnelo, K Arulkumaran, M Shanahan

We propose a reinforcement learning architecture that comprises a neural back end and a symbolic front end with the potential to overcome some of the shortcomings associated with both symbolic and neural methods.

Bonus paper from a past life:

Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma

M Garnelo, A Tan,  Z Her, J Yeong, C J Lim, J Chen, K H Lim, A Weber, P Chow, A Chung, L L PJ Ooi, H C Toh,  M Heikenwalder, I O L Ng, A Nardin, Q Chen, J P Abastado, V Chew

Have you ever wondered what role the immune system plays in cancer? Wonder no longer!

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© 2018

Marta Garnelo
 

Contact

martagarnelo (at)  gmail (dot) com

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