102 citations · 120 across the 11 of their papers we have counts for
5 papers · 1 filter
PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI
Yingchen He, Christian D. Weilbach, Martyna E. Wojciechowska +2
Advances in deep generative modeling have made it increasingly plausible to train human-level embodied agents. Yet progress has been limited by the absence of large-scale, real-tim…
Prospective Messaging: Learning in Networks with Communication Delays
Ryan Fayyazi, Christian Weilbach, Frank Wood
Inter-neuron communication delays are ubiquitous in physically realized neural networks such as biological neural circuits and neuromorphic hardware. These delays have significant…
All-in-one simulation-based inference
Manuel Gloeckler, Michael Deistler, Christian Weilbach +2
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference…
Graphically Structured Diffusion Models
Christian Weilbach, William Harvey, Frank Wood
We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solve…
Inferring the Structure of Ordinary Differential Equations
Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach +1
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements. While accurate prediction can be achieved w…