2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Dataset-Dependent Effects of Cross-Depth Aggregation and Soft-Routed Experts in EEG Foundation Model Fine-Tuning
Mingyang Jiang, Yamin Li, Daniel Moyer +3
EEG decoding tasks can rely on different temporal dynamics and cross-channel relationships. We test whether specialized modules improve a fully fine-tuned EEG foundation model by a…
Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations
Peilun Li, Kaiyuan Tan, Daniel Moyer +1
Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial and high-dimensional observat…
Generalization and Memorization in Rectified Flow
Mingxing Rao, Daniel Moyer
Generative models based on the Flow Matching objective, particularly Rectified Flow, have emerged as a dominant paradigm for efficient, high-fidelity image synthesis. However, whil…
Latent Diffusion Inversion Requires Understanding the Latent Space
Mingxing Rao, Bowen Qu, Daniel Moyer
The recovery of training data from generative models ("model inversion") has been extensively studied for diffusion models in the data domain as a memorization/overfitting phenomen…
Score-based Membership Inference on Diffusion Models
Mingxing Rao, Bowen Qu, Daniel Moyer
Membership inference attacks (MIAs) against Diffusion Models (DMs) raise pressing privacy concerns by revealing whether a sample was part of the training set. While existing method…