arXiv AI
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching
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arXiv Machine Learning
Jun 5
Coreset-Induced Conditional Velocity Flow Matching
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.
arXiv Machine Learning
Aug 21
Continuous Adversarial MeanFlow Transfer
arXiv:2608. 19540v1 Announce Type: new Abstract: Training fast generators on new domains with limited data remains challenging for two reasons.
arXiv Machine Learning
Oct 8
MUNITE: Unified Multimodal Latent Inference for Any-to-Any Multimodal Generation
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.
Hugging Face Trending Papers
Sep 30
NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching
Identifying which speaker a listener is attending to in a noisy room -- the cocktail-party problem -- is the missing ingredient for next-generation hearing aids and brain-computer interfaces: it tells...
arXiv Machine Learning
Oct 3
NEUROTOKEN: Joint Source and Directional AAD with Envelope Decoding via Conditional Flow Matching
The paper introduces NEUROTOKEN, a unified neural network for auditory attention decoding (AAD) that jointly predicts the attended speaker’s direction and source by modeling the conditional likelihood of the attended envelope given EEG. It employs a conditional flow‑matching head (ATTUNEFLOW) and two inference‑time ensembles (QUADTRACK and ENV‑FLOW) to improve source‑AAD accuracy and reduce variance across subjects. Experiments on KU Leuven, DTU, and NJU datasets show significant gains over existing baselines and reveal that prior direction‑AAD results overestimate performance under stricter protocols.
arXiv AI
Oct 15
Generalizable field of view extension for diffusion MRI via conditional flow matching
arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...