The paper investigates how the usefulness of training samples, as determined by coreset selection, depends on the learner rather than just the data. Experiments on ImageNet-100 and ImageNet-1k show that changing model width, input grid, stride, and architecture (e.g., ResNet vs. ViT) shifts the crossover point where different selection criteria (easy-first vs. geometric coverage) become optimal. These findings demonstrate that the relative value of a fixed subset of samples varies with the target learner’s capacity and structure, and that selection strategies must be tuned to the specific model they will train.
By Yangze Liu, Xiao-Long Yin, Zhongyi Han
arXiv:2605. 31244v2 Announce Type: replace Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance.
By Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf, Samuel Tovey, Christian Holm
The paper introduces a Focal Log-Frequency Loss (f-loss) to counteract the spectral imbalance in pixel-space flow matching, where low frequencies dominate training. By balancing learning signals across frequencies and combining early frequency-domain supervision with later pixel-space refinement, the method accelerates convergence by up to 40% and improves FID and perceptual fidelity across multiple model scales. It requires no architectural changes and can replace existing flow matching losses as a drop‑in solution.
By Lucas Degeorge, Paul Couairon, Arijit Ghosh, Alexei A. Efros, David Picard, Vicky Kalogeiton
The paper introduces Multiscale Spectral Rate‑Distortion (MS‑SRD), a training‑free method that predicts the required bottleneck channel width for convolutional autoencoders at user‑specified spatial cuts, using only training images and a normalized mean‑squared error bound. MS‑SRD’s covariance‑tail rule is exact for shared linear block‑convolutional autoencoders under squared error, and a nested‑scale dominance result allows reporting an activation‑parameter Pareto frontier alongside the minimal‑latent candidate. Across thirteen grayscale datasets, the method achieves a 0.84% mean absolute percentage error in latent‑size prediction, with most predictions exact or within one channel, and demonstrates comparable performance to retrospective external widths in deployable comparisons without any training of a selector.
By Guannan Guo
arXiv:2606. 00677v1 Announce Type: new Abstract: Fourier Neural Operators are often assumed to generalize across spatial resolutions, enabling training on a coarse grid and deployment on a finer grid.
By Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
arXiv:2610.07419v1 Announce Type: new
Abstract: Implicit neural representations (INRs) are shaped by the spectral structure induced by their input encodings and activation functions. Existing methods...
By Tamir Shor, Or Litany, Alex Bronstein