Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.

However, existing DNNs are mostly designed to analyze neural responses to static images, relying on feedforward structures and lacking physiological neuronal mechanisms. There is limited insight into how the visual cortex represents natural movie stimuli that contain context-rich information.

In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],

The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query.

Unsurprisingly, GNNs as MPNNs have been analyzed from multiple theoretical perspectives.

Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models enjoying the key ingredient behind expressive generative models: continuous latent variables (L Vs). PICs are symbolic computational graphs defining continuous L V models as hierarchies of functions that are summed and multiplied together, or integrated over some L Vs. They are tractable if L Vs can be analytically integrated out, otherwise they can be approximated by tractable probabilistic circuits (PC) encoding a hierarchical numerical quadrature process, called QPCs. So far, only tree-shaped PICs have been explored, and training them via numerical quadrature requires memory-intensive processing at scale. In this paper, we address these issues, and present: (i) a pipeline for building DAG-shaped PICs out of arbitrary variable decompositions, (ii) a procedure for training PICs using tensorized circuit architectures, and (iii) neural functional sharing techniques to allow scalable training.