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Found 9 papers, 0 papers with code
Filter-Aware Model-Predictive Control
Partially-observable problems pose a trade-off between reducing costs and gathering information.
PRISM: Probabilistic Real-Time Inference in Spatial World Models
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception.
Tracking and Planning with Spatial World Models
We introduce a method for real-time navigation and tracking with differentiably rendered world models.
Less Suboptimal Learning and Control in Variational POMDPs
We examine the effect of the conditioning gap on model-based reinforcement learning with variational world models.
Model-based Reinforcement Learning
reinforcement-learning
+2
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO).
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model.
Approximate Bayesian inference in spatial environments
Model-based approaches bear great promise for decision making of agents interacting with the physical world.
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space.
3D Deep Learning for Biological Function Prediction from Physical Fields
In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.
