3 papers
cs.LG2026
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…
cs.LG2024
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…
cs.LG2024
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…