Learning Collective Dynamics with Differentiable Gaussian Representations
Researchers propose Differentiable Gaussian Dynamics to learn population behavior from aggregate counts, beating a DeepAR baseline.
The paper introduces Differentiable Gaussian Dynamics, which represents heterogeneous response propensities with a Gaussian mixture, aggregates contact intensity and behavior probabilities differentiably, and updates later responses through feedback recurrence. On windows from KuaiRand-Pure and Online Retail II, it reports lower joint behavioral negative log-likelihood than a DeepAR adaptation with a joint-behavior head. In Retail 2010, one-day behavioral-count MAE is 4.71 versus 6.88. Learning the distribution reduces behavioral NLL by 10.82% versus a fixed Gaussian on one KuaiRand window, and removing feedback raises joint KL from 0.0340 to 0.2577.
- DGD learns population response distributions from aggregate counts.
- Retail 2010 one-day count MAE is 4.71 versus 6.88 for DeepAR.
- Learned distribution cuts behavioral NLL 10.82% versus a fixed Gaussian.
- Removing feedback raises joint KL from 0.0340 to 0.2577.
From the original article
1 min read at arxiv.orgCollective 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.
By Jianxiang Ma · arxiv.org
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