Danilo Jimenez Rezende

According to our database1, Danilo Jimenez Rezende authored at least 43 papers between 2011 and 2018.

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Bibliography

2018
Taming VAEs.
CoRR, 2018

Learning models for visual 3D localization with implicit mapping.
CoRR, 2018

Consistent Generative Query Networks.
CoRR, 2018

Neural Processes.
CoRR, 2018

Conditional Neural Processes.
CoRR, 2018

A Probabilistic U-Net for Segmentation of Ambiguous Images.
CoRR, 2018

Generative Temporal Models with Spatial Memory for Partially Observed Environments.
CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.
CoRR, 2018

Optimizing Slate Recommendations via Slate-CVAE.
CoRR, 2018

Learning and Querying Fast Generative Models for Reinforcement Learning.
CoRR, 2018

Conditional Neural Processes.
Proceedings of the 35th International Conference on Machine Learning, 2018

Generative Temporal Models with Spatial Memory for Partially Observed Environments.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.
CoRR, 2017

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions.
CoRR, 2017

Variational Memory Addressing in Generative Models.
CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.
CoRR, 2017

Generative Temporal Models with Memory.
CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Memory Addressing in Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
One-Shot Generalization in Deep Generative Models.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
CoRR, 2016

Variational inference for Monte Carlo objectives.
CoRR, 2016

Variational Intrinsic Control.
CoRR, 2016

Towards Conceptual Compression.
CoRR, 2016

Normalizing Flows on Riemannian Manifolds.
CoRR, 2016

Interaction Networks for Learning about Objects, Relations and Physics.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Towards Conceptual Compression.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Interaction Networks for Learning about Objects, Relations and Physics.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

One-Shot Generalization in Deep Generative Models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Variational Inference for Monte Carlo Objectives.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Towards Principled Unsupervised Learning.
CoRR, 2015

Variational Inference with Normalizing Flows.
CoRR, 2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.
CoRR, 2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Inference with Normalizing Flows.
Proceedings of the 32nd International Conference on Machine Learning, 2015

DRAW: A Recurrent Neural Network For Image Generation.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Stochastic variational learning in recurrent spiking networks.
Front. Comput. Neurosci., 2014

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models.
CoRR, 2014

Semi-Supervised Learning with Deep Generative Models.
CoRR, 2014

Semi-supervised Learning with Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
Proceedings of the 31th International Conference on Machine Learning, 2014

2011
Variational Learning for Recurrent Spiking Networks.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011


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