We introduce a training strategy that addresses vanishing gradients in quantum neural networks, and makes better use of the resources provided by noisy intermediate-scale quantum (NISQ) devices.
Learning to Fly
Deep Model-Based Reinforcement Learning in the real world
In this work we show how to learn neural network policy for thrust-attitude control of a self-built drone via model-based reinforcement learning and variational inference methods.
Learning Flat Manifold of VAEs
How to create latent spaces without nasty curvatures
The powerful neural-network encoder/decoder in a VAE typically creates crooked latent spaces. This method, published at ICML 2020, regularises that.
Approximate Bayesian inference in spatial environments
How to reason about space and moving agents
In this post, we will get familiar with a flexible probabilistic platform for spatial reasoning.
Learning Hierarchical Priors in VAEs
A constrained optimisation approach
We address the issue of learning informative latent representations of data. In the normal VAE, the latent space prior is a standard normal distribution. This over-regularises the posterior distribution, resulting in latent representations that do not represent well the structure of the data. This post, describing our 2019 NeurIPS publication, proposes and demonstrates a solution by using an hierarchical latent space prior.
How to Learn Functions on Sets with Neural Networks
And how to choose your aggregation
If you input a vector of data in a neural network, the order of the elements matters. But ssometimes the order doesn't carry any useful inforamtion: sometimes we are interested in working on sets of data. In this post, we will look into functions on sets, and how to learn them with the help of neural networks.
Approximate Geodesics for Deep Generative Models
How to efficiently find the shortest path in latent space
Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source—the latent space—to a more complex distribution—corresponding to the distribution from which is data is sampled.
Typically, the data set is sparse, while the latent space is compact. Consequently, points that are separated by low-density regions in observation space will be pushed together in latent space, and the spaces get distored. In effect, stationary distances in the latent space are poor proxies for similarity in the observation space. How can this be solved?
Network Architecture Optimisation
The Bayesian way
Intuition and experience. Probably, that's the answer you would get if you happen to ask deep learning engineers how they chose the hyperparameters of a neural network. Depending on their familiarity with the problem, they might have done some good three to five full dataset runs until a satisfactory result popped up. Now, you might say, we surely could automate this, right, after all we do it implicitly in our heads? Well, yes, we definitely could, but should we?
Deep Variational Bayes Filter
DVBF: filter to learn what to filter
Machine-learning algorithms thrive in environments where data is abundant. In the land of scarce data, blessed are those who have simulators. The recent successes in Go or Atari games would be much harder to achieve without the ability to parallelise millions of perfect game simulations.
About
Machine Learning Research Lab, Volkswagen Group
Established in 2016, Volkswagen Group Machine Learning Research Lab, located in Munich, was set up as a fundamental research lab on topics close to what we think artificial intelligence is about: machine learning and probabilistic inference, efficient exploration, and optimal control.
