argmax.ai

Spatial World Models

Real-Time Probabilistic Dense Inference

Spatial World Models

Tracking and Planning in 3D

Latent Matters

Learning Deep State-Space Models

10toGO: Virtual thinkathon for Sustainable Development Goals

Or: what we do when we don't do science.

Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF

Scaling generative spatial models to the real world

Less Suboptimal Learning and Control in Variational POMPDs

How to fix model-based RL by doing the obvious.

A Tale of Gaps

Inference Suboptimalities in Amortised Variational Inference

Continual Learning with Bayesian Neural Networks for Non-Stationary Data

Adapting to changing environments

Estimating Fingertip Forces, Torques, and Object Curvatures from Fingernail Images

Layerwise learning for quantum neural networks

Solving vanishing gradients in quantum neural networks

Learning to Fly

Deep Model-Based Reinforcement Learning in the real world

Learning Flat Manifold of VAEs

How to create latent spaces without nasty curvatures

Approximate Bayesian inference in spatial environments

How to reason about space and moving agents

Learning Hierarchical Priors in VAEs

A constrained optimisation approach

How to Learn Functions on Sets with Neural Networks

And how to choose your aggregation

Approximate Geodesics for Deep Generative Models

How to efficiently find the shortest path in latent space

Network Architecture Optimisation

The Bayesian way

Deep Variational Bayes Filter

DVBF: filter to learn what to filter

About

Machine Learning Research Lab, Volkswagen Group