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Tag: latent

  • 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…

  • Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models

    Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models arXiv:2602.23518v1 Announce Type: new Abstract: Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework for disentangling representations of thermal Sunyaev-Zel’dovich (tSZ) maps of dark matter halos. We introduce halo mass…

  • 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…

  • Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation

    Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation arXiv:2602.02633v1 Announce Type: new Abstract: Often, constraints arise in deployment settings where even lightweight parameter updates e.g. parameter-efficient fine-tuning could induce model shift or tuning instability. We study test-time adaptation of foundation models for few-shot classification under a completely frozen-model regime, where additionally, no…

  • Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators

    Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators arXiv:2601.20888v1 Announce Type: new Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $tilde{A}$.…

  • Towards Latent Diffusion Suitable For Text

    Towards Latent Diffusion Suitable For Text arXiv:2601.16220v1 Announce Type: cross Abstract: Language diffusion models aim to improve sampling speed and coherence over autoregressive LLMs. We introduce Neural Flow Diffusion Models for language generation, an extension of NFDM that enables the straightforward application of continuous diffusion models to discrete state spaces. NFDM learns a multivariate forward…

  • A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour

    A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour arXiv:2512.14713v1 Announce Type: cross Abstract: Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity across individual travellers, both in their underlying preferences and in how…

  • Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifolds

    Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifolds arXiv:2512.10407v1 Announce Type: new Abstract: This paper introduces a new probabilistic framework for supervised learning in neural systems. It is designed to model complex, uncertain systems whose random outputs are strongly non-Gaussian given deterministic inputs. The architecture itself is…

  • Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions

    Latent Nonlinear Denoising Score Matching for Enhanced Learning of Structured Distributions arXiv:2512.06615v1 Announce Type: new Abstract: We present latent nonlinear denoising score matching (LNDSM), a novel training objective for score-based generative models that integrates nonlinear forward dynamics with the VAE-based latent SGM framework. This combination is achieved by reformulating the cross-entropy term using the approximate…

  • Learning Causality for Longitudinal Data

    Learning Causality for Longitudinal Data arXiv:2512.04980v1 Announce Type: new Abstract: This thesis develops methods for causal inference and causal representation learning (CRL) in high-dimensional, time-varying data. The first contribution introduces the Causal Dynamic Variational Autoencoder (CDVAE), a model for estimating Individual Treatment Effects (ITEs) by capturing unobserved heterogeneity in treatment response driven by latent risk…

  • 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…

  • Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling

    Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling arXiv:2510.01329v1 Announce Type: new Abstract: Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an ‘information void’ where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented…

  • Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

    Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC) arXiv:2509.12401v1 Announce Type: cross Abstract: Physics-aware deep learning (PADL) has gained popularity for use in complex spatiotemporal dynamics (field evolution) simulations, such as those that arise frequently in computational modeling of energetic materials (EM). Here, we show that…

  • Coconut: A Framework for Latent Reasoning in LLMs

    Coconut: A Framework for Latent Reasoning in LLMs Explaining Coconut (Training Large Language Models to Reason in a Continuous Latent Space) in simple terms The post Coconut: A Framework for Latent Reasoning in LLMs appeared first on Towards Data Science. Youssef Farag Go to original source

  • LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process

    LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process arXiv:2507.22493v1 Announce Type: new Abstract: We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The core idea is to construct a stochastic mapping from the input to a high-dimensional…

  • Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

    Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets arXiv:2507.00298v1 Announce Type: new Abstract: This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables…

  • Latent Guided Sampling for Combinatorial Optimization

    Latent Guided Sampling for Combinatorial Optimization arXiv:2506.03672v1 Announce Type: new Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging. Recent Neural Combinatorial Optimization methods leverage deep learning to learn solution strategies, trained via Supervised or Reinforcement Learning (RL). While promising, these…

  • Identifiability of latent causal graphical models without pure children

    Identifiability of latent causal graphical models without pure children arXiv:2505.18410v1 Announce Type: new Abstract: This paper considers a challenging problem of identifying a causal graphical model under the presence of latent variables. While various identifiability conditions have been proposed in the literature, they often require multiple pure children per latent variable or restrictions on the…

  • On the Identifiability of Causal Abstractions

    On the Identifiability of Causal Abstractions arXiv:2503.10834v1 Announce Type: new Abstract: Causal representation learning (CRL) enhances machine learning models’ robustness and generalizability by learning structural causal models associated with data-generating processes. We focus on a family of CRL methods that uses contrastive data pairs in the observable space, generated before and after a random, unknown…

  • Identifying metric structures of deep latent variable models

    Identifying metric structures of deep latent variable models arXiv:2502.13757v1 Announce Type: new Abstract: Deep latent variable models learn condensed representations of data that, hopefully, reflect the inner workings of the studied phenomena. Unfortunately, these latent representations are not statistically identifiable, meaning they cannot be uniquely determined. Domain experts, therefore, need to tread carefully when interpreting…

  • A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems

    A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems arXiv:2501.08423v1 Announce Type: new Abstract: Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In particular, StODE’s often cannot be solved with traditional explicit time integration schemes and one must resort to costly implicit methods to…