arXiv:2604. 24196v3 Announce Type: replace-cross Abstract: This paper studies the identifiability and stability of drifting fields in the framework of Generative Modeling via Drifting.
By HakGeun Lee, Hyonho Chun
arXiv:2608. 01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations.
By Sam Andersson, Ricky Mol\'en
arXiv:2606. 13796v1 Announce Type: cross Abstract: Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift away from the true data distribution.
By Na\"il B. Khelifa, Richard E. Turner, Ramji Venkataramanan
arXiv:2609. 18901v1 Announce Type: cross Abstract: In physics-informed machine learning, a target function $u^*$ is learned from noisy value observations $y_i=u^*(x_i)+ \varepsilon_i$, together with differential information, given either by noisy observations $d_j=(Du^*)(z_j)+\xi_j$ or by a known physical constraint $Du^*=v$.
By Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti, Lorenzo Rosasco
arXiv:2605.22795v3 Announce Type: replace-cross
Abstract: We analyze finite-particle drifting models for one-step generative modeling. For a conservative velocity given by the difference of the kerne...
By Krishnakumar Balasubramanian
arXiv:2608. 07924v1 Announce Type: cross Abstract: Drifting models are a recent class of one-step generative models that evolve the model distribution during training using a predefined sample-based drift field.
By Drake Brown, Yuhao Huang, Shih-Hsin Wang, Bao Wang