arXiv Machine Learning
Learning Through Energy Refinement and Manifold Projection: A Cooperative EBM-AE Framework
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arXiv Machine Learning
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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...
arXiv AI
Jun 2
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
arXiv:2606. 00078v1 Announce Type: cross Abstract: Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints.
arXiv Computer Vision
Sep 1
GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling
arXiv:2608.29335v1 Announce Type: new Abstract: Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on th...
arXiv Computer Vision
Sep 1
Off-Manifold Refinement: Guiding Video Generators with a Frozen World Model
arXiv:2608.29904v1 Announce Type: new Abstract: Modern video generators routinely fail at physical dynamics: objects float, trajectories violate gravity, contacts vanish. Standard denoising and flow-...
arXiv Computer Vision
Sep 1
Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI
arXiv:2608.28787v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rec...
arXiv Machine Learning
Jul 21
Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations
arXiv:2607. 16725v1 Announce Type: cross Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant.