Search Results for author: Danilo Rezende

Found 13 papers, 4 papers with code

Symmetry-Based Representations for Artificial and Biological General Intelligence

no code implementations17 Mar 2022 Irina Higgins, Sébastien Racanière, Danilo Rezende

In this review article we are going to argue that symmetry transformations are a fundamental principle that can guide our search for what makes a good representation.

Representation Learning

From data to functa: Your data point is a function and you should treat it like one

no code implementations28 Jan 2022 Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Rezende, Dan Rosenbaum

A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location.

Imputation Novel View Synthesis

Integrable Nonparametric Flows

no code implementations3 Dec 2020 David Pfau, Danilo Rezende

This reverses the conventional task of normalizing flows -- rather than being given samples from a unknown target distribution and learning a flow that approximates the distribution, we are given a perturbation to an initial distribution and aim to reconstruct a flow that would generate samples from the known perturbed distribution.

Consistent Jumpy Predictions for Videos and Scenes

no code implementations ICLR 2019 Ananya Kumar, S. M. Ali Eslami, Danilo Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

3D Scene Reconstruction Video Prediction

Towards a Definition of Disentangled Representations

1 code implementation5 Dec 2018 Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, Alexander Lerchner

Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.

Representation Learning

Interaction Networks for Learning about Objects, Relations and Physics

6 code implementations NeurIPS 2016 Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu

Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system.

Normalizing Flows on Riemannian Manifolds

no code implementations7 Nov 2016 Mevlana C. Gemici, Danilo Rezende, Shakir Mohamed

In spite of the multitude of algorithms available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that scale to large n (e. g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds.

Density Estimation Protein Folding

Towards Principled Unsupervised Learning

no code implementations19 Nov 2015 Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim Lillicrap, Oriol Vinyals

Supervised learning is successful because it can be solved by the minimization of the training error cost function.

Domain Adaptation

Cannot find the paper you are looking for? You can Submit a new open access paper.