18 citations · 23 across the 6 of their papers we have counts for
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…
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…
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…