SOURCE-LINKED INTELLIGENCE
Learning Collective Dynamics with Differentiable Gaussian Representations
Collective responses depend on individual differences, contact opportunities, and accumulated experience. Learning their dynamics from aggregate counts requires connecting a population's response distribution to both current observations and future behavior. We introduce Differentiable Gaussian Dynamics (DGD), which learns this connection through three components: a Gaussian mixture representing heterogeneous response propensities, differentiable aggregation of contact intensity and behavioral probabilities, and feedback recurrence that updates subsequent responses. Reparameterized integration
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T17:08:53.000Z
First collected: 2026-09-24T08:22:30.429Z. This is not the publication date.