arXiv:2406. 07775v2 Announce Type: replace Abstract: Multimode optical fibres are hair-thin strands of glass that efficiently transport light.
arXiv:2609.14869v1 Announce Type: cross Abstract: The multimode fiber is among the thinnest imaging conduits available, carrying hundreds to thousands of spatial modes through a cross-section compara...
arXiv:2603. 18846v3 Announce Type: replace-cross Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL).
arXiv:2602.06042v2 Announce Type: replace-cross Abstract: The Moore-Penrose Pseudo-inverse (PInv) serves as the fundamental solution for linear systems. In this paper, we propose a natural generaliza...
The study investigates how aligning the representational geometry of artificial neural networks can improve bidirectional predictivity with biological neural responses. By applying spectral regularization during training of self‑supervised contrastive vision models, the authors increased reverse predictivity by 55% while only modestly reducing forward predictivity. The adjustments also lowered effective dimensionality and reorganized the shared representational subspace, making forward and reverse predictivity more symmetric at intermediate spectral exponents.
SlowFast‑SCI introduces a dual‑speed deep‑unfolding framework for spectral compressive imaging that combines a slow, pre‑trained backbone with a fast, test‑time adaptation stage. The slow phase distills a priors‑based model into a compact fast‑unfolding network, while the fast phase embeds lightweight modules that self‑supervise at test time without retraining the backbone. This design yields significant reductions in parameters and FLOPs, improves out‑of‑distribution PSNR by up to 5.79 dB, and accelerates adaptation four‑fold, all while remaining modular enough to integrate with any existing deep‑unfolding system.
arXiv:2607. 15693v1 Announce Type: cross Abstract: We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters.
arXiv:2606. 03262v1 Announce Type: new Abstract: Neural operators learn mappings between infinite-dimensional function spaces and provide a data-driven surrogate modeling paradigm for parametric partial differential equations (PDEs).
arXiv:2607. 25576v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering the initial pressure distribution from sparse-view sensor measurements remains an ill-posed inverse problem.
The study investigates how the geometry of representations in artificial neural networks can be steered to improve bidirectional alignment with biological neural responses. By applying spectral regularization during training of self‑supervised contrastive vision models, the authors increased reverse predictivity by 55% while only modestly reducing forward predictivity. The changes also lowered effective dimensionality and reorganized the shared subspace, making forward and reverse predictivity more symmetric at certain spectral exponents.
arXiv:2511. 17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations.
arXiv:2607. 16295v1 Announce Type: cross Abstract: Mechanistic interpretability has made significant strides in understanding neural network representations, with sparse dictionary learning (SDL) methods, most prominently sparse autoencoders, as a central paradigm.
arXiv:2602. 10680v2 Announce Type: replace-cross Abstract: Many real-world datasets contain hidden structure that cannot be detected by simple linear correlations between input features.