6 papers
MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
Lake Yang, Antonio Malpica-Morales, Frank Ioannis Papadakis Wood +1
Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long horizons. We propose MPINeuralO…
Discrete Meanflow Training Curriculum
Chia-Hong Hsu, Frank Wood
Flow-based image generative models exhibit stable training and produce high quality samples when using multi-step sampling procedures. One-step generative models can produce high q…
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…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
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…
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…