arXiv Machine Learning
Jun 4
Geometry-Preserving Encoder/Decoder in Latent Generative Models
arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.
Variational autoencoders with latent high-dimensional steady geometric flows for dynamics
arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.
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arXiv AI
Sep 2
Geometry-aware Latent Autoregressive Generative Model for PDEs in Complex Domains
The paper introduces GeoLAMP, a Geometry-aware Latent Autoregressive generative Model designed to solve multiphysics partial differential equations in highly irregular, micro‑scale tortuous geometries. GeoLAMP employs a dual‑encoder graph architecture to capture both global topology and fine‑scale geometry, transforms real‑space fields into compact latent representations, and uses a causal self‑attention transformer with flow matching for stable, scalable block‑wise autoregressive prediction. The model is evaluated on three benchmark datasets—reactive flow, heat convection, and elasticity—showing consistently low errors across the entire rollout horizon.
arXiv Machine Learning
Jul 27
Latent PDE mapping for efficient physics-informed learning across geometries with limited data
arXiv:2607. 22215v1 Announce Type: new Abstract: In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient geometric generalization with sparse training data.
arXiv Machine Learning
Jul 3
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
arXiv:2603. 12676v3 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable.
arXiv Machine Learning
Jun 17
Geodesic Calculus on Implicitly Defined Latent Manifolds
arXiv:2510. 09468v3 Announce Type: replace Abstract: Latent manifolds of autoencoders provide low-dimensional representations of data, which can be studied from a geometric perspective.
arXiv AI
Jun 8
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
arXiv:2606. 07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for large-scale training data for foundation models.
arXiv Machine Learning
Sep 1
Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
arXiv:2608.29867v1 Announce Type: new Abstract: Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear enc...
Hugging Face Trending Papers
Jul 21
Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support.
arXiv Machine Learning
Sep 1
Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions
arXiv:2608.31028v1 Announce Type: cross Abstract: Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and co...
arXiv Computer Vision
Sep 23
Latent Dataset Distillation for Human Motion Prediction
The paper introduces a latent dataset distillation framework for human motion prediction, addressing the limitations of traditional gradient matching by incorporating a learned motion prior. Motions are compressed using a residual‑quantized variational autoencoder, and distillation updates only a latent bank while keeping the decoder frozen, ensuring synthetic motions remain plausible. Experiments on Human3.6M, CMU, and 3DPW datasets demonstrate that this method outperforms direct gradient matching in most settings and yields more realistic synthetic motions.
arXiv Machine Learning
Sep 14
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
The paper investigates nonlinear dimensionality reduction for Bayesian optimisation (BO) by transforming high‑dimensional black‑box optimisation problems into a sequence of low‑dimensional latent‑space BO (LSBO) tasks. It extends earlier linear embedding approaches by using variational autoencoders (VAEs), deep metric loss, and adaptive retraining to better capture nonlinear structure, and couples LSBO with sequential domain reduction (SDR‑LSBO) to progressively narrow search domains. Experiments on GPU‑accelerated BoTorch with Matérn‑5/2 Gaussian‑process surrogates show that VAE‑based LSBO outperforms adaptive linear embeddings, and the authors provide a theoretical analysis of latent‑space error versus representation gap under PAC‑Bayes conditions.
arXiv Computer Vision
2d ago
Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models
arXiv:2610.01942v1 Announce Type: new Abstract: Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of...