The information bottleneck (IB) trades compression of an input $X$ against prediction of a target $Y$ through a multiplier $β=1/λ$, yet each operating point is usually served by training, or fully con...
MUNITE is a latent‑variable framework that unifies encoding and latent generation for any‑to‑any multimodal generation. By treating different amounts of observed evidence as varying inference problems, it models the conditional distribution over a shared latent representation, using a single conditional flow model. The approach employs self‑distillation with conditional flow matching to learn from incomplete data, achieving competitive or superior generation quality and coherence across datasets such as PolyMNIST‑D‑Q, FFHQ64, and image‑text‑audio.
By Kyeongmin Yeo, Minhyuk Sung
arXiv:2605. 12951v2 Announce Type: replace-cross Abstract: We propose Coreset-Induced Conditional Velocity Flow Matching (CCVFM), a generative model that augments hierarchical rectified flow with a data-informed source distribution.
By Xiao Wang, Zihua She, Jianxi Su
arXiv:2607. 01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\in\mathbb{R}^{m\times n}$ with $m<n$, and inferring a sparse code $\mathbf{x}\in\mathbb{R}^n$ from $\mathbf{h}\approx\mathbf{W}\mathbf{x}$.
By Rodrigo Mendoza-Smith
The paper introduces Conditional Predictive Sufficient Statistics (CPSS) as a formal way to capture useful visual representations that preserve latent factors shared with future data while discarding noise. It shows that predicting the next image patch embedding with a cosine loss approximates maximum likelihood under a von Mises-Fisher model, and that stop‑gradient alone does not enforce sufficiency. Experiments on MNIST and CIFAR‑10 with small causal Transformers demonstrate that CPSS readouts outperform intermediate blocks, while removing stop‑gradient collapses embedding rank even when the pretext loss appears perfect.
By Yuzhou Hong
The paper introduces the Velocity Ambiguity Profile (VAP), a time‑local Bayes‑risk floor for velocity prediction in flow matching, and decomposes the loss into an irreducible ambiguity term and a reducible excess term. It provides statistical benchmarks for intrinsic local‑regression and Gaussian‑mixture scenarios, and validates the diagnostic with controlled experiments and CIFAR‑100 DINOv2 feature flow, showing that guided time sampling can reduce velocity loss and Fréchet distance.
By Yunpeng Mei, Xiaowen Zhu, Chenyu Wang, Chenbo Xin, Hongjie Cao, Jiamin Wang, Jie Chen, Gang Wang