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Found 4 papers, 2 papers with code
Wasserstein proximal operators describe score-based generative models and resolve memorization
Via a Cole-Hopf transformation and taking advantage of the fact that the cross-entropy can be related to a linear functional of the density, we show that the HJB equation is an uncontrolled FP equation.
Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
In the growing domain of scientific machine learning, in-context operator learning has shown notable potential in building foundation models, as in this framework the model is trained to learn operators and solve differential equations using prompted data, during the inference stage without weight updates.
In-Context Operator Learning with Data Prompts for Differential Equation Problems
This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update.
Fault-Tolerant Deep Learning: A Hierarchical Perspective
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics.
