mailitics

Tag: energy

  • 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…

  • Energy-Tweedie: Score meets Score, Energy meets Energy

    Energy-Tweedie: Score meets Score, Energy meets Energy arXiv:2512.23818v1 Announce Type: new Abstract: Denoising and score estimation have long been known to be linked via the classical Tweedie’s formula. In this work, we first extend the latter to a wider range of distributions often called “energy models” and denoted elliptical distributions in this work. Next, we…

  • Minima and Critical Points of the Bethe Free Energy Are Invariant Under Deformation Retractions of Factor Graphs

    Minima and Critical Points of the Bethe Free Energy Are Invariant Under Deformation Retractions of Factor Graphs arXiv:2510.05380v1 Announce Type: new Abstract: In graphical models, factor graphs, and more generally energy-based models, the interactions between variables are encoded by a graph, a hypergraph, or, in the most general case, a partially ordered set (poset). Inference…

  • Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling

    Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling arXiv:2509.03726v1 Announce Type: new Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions $mu_{text{target}}(x) propto exp(-E(x)/T)$, is fundamental to many scientific applications yet computationally challenging due to complex, high-dimensional energy landscapes. Existing approaches applying modern generative models to Boltzmann distributions either require…

  • Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis

    Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I…

  • Expected Free Energy-based Planning as Variational Inference

    Expected Free Energy-based Planning as Variational Inference arXiv:2504.14898v1 Announce Type: new Abstract: We address the problem of planning under uncertainty, where an agent must choose actions that not only achieve desired outcomes but also reduce uncertainty. Traditional methods often treat exploration and exploitation as separate objectives, lacking a unified inferential foundation. Active inference, grounded in…

  • FEAT: Free energy Estimators with Adaptive Transport

    FEAT: Free energy Estimators with Adaptive Transport arXiv:2504.11516v1 Announce Type: new Abstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation — a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled…

  • Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling

    Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling arXiv:2504.10612v1 Announce Type: cross Abstract: Generative models often map noise to data by matching flows or scores, but these approaches become cumbersome for incorporating partial observations or additional priors. Inspired by recent advances in Wasserstein gradient flows, we propose Energy Matching, a framework that…

  • A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize)

    A Derivation and Application of Restricted Boltzmann Machines (2024 Nobel Prize) Investigating Geoffrey Hinton’s Nobel Prize-winning work and building it from scratch using PyTorch One recipient of the 2024 Nobel Prize in Physics was Geoffrey Hinton for his contributions in the field of AI and machine learning. A lot of people know he worked on neural…

  • Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations

    Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations arXiv:2412.07265v1 Announce Type: new Abstract: In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of…

  • Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces

    Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces arXiv:2412.01019v1 Announce Type: new Abstract: Energy-based models (EBMs) offer a flexible framework for probabilistic modelling across various data domains. However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling…