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Tag: space

  • Time-Aware Latent Space Bayesian Optimization

    Time-Aware Latent Space Bayesian Optimization arXiv:2603.00935v1 Announce Type: new Abstract: Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative model. However, most LSBO methods assume a fixed objective, whereas real design campaigns often face temporal drift (e.g., evolving preferences or…

  • Stochastic Gradient Variational Inference with Price’s Gradient Estimator from Bures-Wasserstein to Parameter Space

    Stochastic Gradient Variational Inference with Price’s Gradient Estimator from Bures-Wasserstein to Parameter Space arXiv:2602.18718v1 Announce Type: new Abstract: For approximating a target distribution given only its unnormalized log-density, stochastic gradient-based variational inference (VI) algorithms are a popular approach. For example, Wasserstein VI (WVI) and black-box VI (BBVI) perform gradient descent in measure space (Bures-Wasserstein space)…

  • Latent space analysis and generalization to out-of-distribution data

    Latent space analysis and generalization to out-of-distribution data arXiv:2511.15010v1 Announce Type: new Abstract: Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting textit{out-of-distribution} (OOD) data for deep learning systems continues to…

  • Towards universal property prediction in Cartesian space: TACE is all you need

    Towards universal property prediction in Cartesian space: TACE is all you need arXiv:2509.14961v1 Announce Type: new Abstract: Machine learning has revolutionized atomistic simulations and materials science, yet current approaches often depend on spherical-harmonic representations. Here we introduce the Tensor Atomic Cluster Expansion and Tensor Moment Potential, the first unified framework formulated entirely in Cartesian space…

  • The Beauty of Space-Filling Curves: Understanding the Hilbert Curve

    The Beauty of Space-Filling Curves: Understanding the Hilbert Curve A quick journey from theory to implementation and application The post The Beauty of Space-Filling Curves: Understanding the Hilbert Curve appeared first on Towards Data Science. Paul Fröhling Go to original source

  • Quantum-inspired probability metrics define a complete, universal space for statistical learning

    Quantum-inspired probability metrics define a complete, universal space for statistical learning arXiv:2508.21086v1 Announce Type: new Abstract: Comparing probability distributions is a core challenge across the natural, social, and computational sciences. Existing methods, such as Maximum Mean Discrepancy (MMD), struggle in high-dimensional and non-compact domains. Here we introduce quantum probability metrics (QPMs), derived by embedding probability…

  • Stochastic dynamics learning with state-space systems

    Stochastic dynamics learning with state-space systems arXiv:2508.07876v1 Announce Type: new Abstract: This work advances the theoretical foundations of reservoir computing (RC) by providing a unified treatment of fading memory and the echo state property (ESP) in both deterministic and stochastic settings. We investigate state-space systems, a central model class in time series learning, and establish…

  • Tensor State Space-based Dynamic Multilayer Network Modeling

    Tensor State Space-based Dynamic Multilayer Network Modeling arXiv:2506.02413v1 Announce Type: new Abstract: Understanding the complex interactions within dynamic multilayer networks is critical for advancements in various scientific domains. Existing models often fail to capture such networks’ temporal and cross-layer dynamics. This paper introduces a novel Tensor State Space Model for Dynamic Multilayer Networks (TSSDMN), utilizing…

  • From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC

    From Physics to Probability: Hamiltonian Mechanics for Generative Modeling and MCMC Phase space of a nonlinear pendulum. Photo by the author. Hamiltonian mechanics is a way to describe how physical systems, like planets or pendulums, move over time, focusing on energy rather than just forces. By reframing complex dynamics through energy lenses, this 19th-century physics…

  • Optimization Can Learn Johnson Lindenstrauss Embeddings

    Optimization Can Learn Johnson Lindenstrauss Embeddings arXiv:2412.07242v1 Announce Type: new Abstract: Embeddings play a pivotal role across various disciplines, offering compact representations of complex data structures. Randomized methods like Johnson-Lindenstrauss (JL) provide state-of-the-art and essentially unimprovable theoretical guarantees for achieving such representations. These guarantees are worst-case and in particular, neither the analysis, nor the algorithm,…