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Tag: poisson

  • Metabolic cost of information processing in Poisson variational autoencoders

    Metabolic cost of information processing in Poisson variational autoencoders arXiv:2602.13421v1 Announce Type: new Abstract: Computation in biological systems is fundamentally energy-constrained, yet standard theories of computation treat energy as freely available. Here, we argue that variational free energy minimization under a Poisson assumption offers a principled path toward an energy-aware theory of computation. Our key…

  • A Hitchhiker’s Guide to Poisson Gradient Estimation

    A Hitchhiker’s Guide to Poisson Gradient Estimation arXiv:2602.03896v1 Announce Type: new Abstract: 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…

  • Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models

    Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models arXiv:2503.17809v1 Announce Type: new Abstract: Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextualized word embeddings, which capture deeper meaning and relationships between words. We aim to leverage…

  • Mastering the Poisson Distribution: Intuition and Foundations

    Mastering the Poisson Distribution: Intuition and Foundations You’ve probably used the normal distribution one or two times too many. We all have — It’s a true workhorse. But sometimes, we run into problems. For instance, when predicting or forecasting values, simulating data given a particular data-generating process, or when we try to visualise model output…

  • An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation

    An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation arXiv:2503.00213v1 Announce Type: new Abstract: Physics-informed machine learning is one of the most commonly used methods for fusing physical knowledge in the form of partial differential equations with experimental data. The idea is to construct a loss function where the physical…

  • Probability Distributions: Poisson vs. Binomial Distribution

    Probability Distributions: Poisson vs. Binomial Distribution Using Soccer to Understand the Difference Between Poisson & Binomial: Probability for Data Science Series (3) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source