AI RESEARCH
A hitchhiker's guide to Poisson gradient estimation
arXiv CS.LG
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ArXi:2602.03896v2 Announce Type: replace-cross Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first systematic comparison of these methods, along with practical guidance for practitioners.