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.
A Gentle Introduction to Autoencoders & Latent Space
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context.
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arXiv Machine Learning
Jun 25
Variational Autoencoder Layer
arXiv:2606. 25900v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space.
arXiv Machine Learning
Jun 17
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.
arXiv AI
Jun 8
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
arXiv:2604. 02029v2 Announce Type: replace Abstract: Latent space is rapidly emerging as a native substrate for language-based models.
arXiv Machine Learning
Jul 28
Soft-Constrained Optimization of Latent Space in Variational Autoencoders
arXiv:2607. 23751v1 Announce Type: new Abstract: The usefulness of a variational autoencoder (VAE) depends on two properties of its latent space that are hard to obtain together: high encoding capacity in the individual latent variables, and a low-dimensional, disentangled organization of those variables.
Hugging Face Trending Papers
Jun 24
Variational Autoencoder Layer
Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications.
arXiv Machine Learning
Jul 7
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.
Hugging Face Trending Papers
Jun 9
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spaces for image generation. However, their reconstruction quality often remains suboptimal, largely because deep VFM representations do not preserve sufficient fine-grained visual detail.
arXiv AI
Jun 16
Efficient Flow Matching using Latent Variables
arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.
arXiv Machine Learning
Jun 3
A Robust Optimization Approach to Sparse Principal Component Analysis
arXiv:2606. 03553v1 Announce Type: cross Abstract: While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dimensional data.
arXiv AI
Jun 11
The Latent Color Subspace: Emergent Order in High-Dimensional Chaos
arXiv:2603. 12261v2 Announce Type: replace-cross Abstract: Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due to limited understanding of how semantic information is encoded.
OpenAI Blog
Nov 8, 2016