Search Results for author:
Found 14 papers, 5 papers with code
DiscoGen: Learning to Discover Gene Regulatory Networks
To accurately identify GRNs, perturbational data is required.
Symmetry-Based Representations for Artificial and Biological General Intelligence
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.
From data to functa: Your data point is a function and you can treat it like one
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.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure.
Integrable Nonparametric Flows
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
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Towards a Definition of Disentangled Representations
Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet.
Interaction Networks for Learning about Objects, Relations and Physics
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
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.
Towards Principled Unsupervised Learning
Supervised learning is successful because it can be solved by the minimization of the training error cost function.
