arXiv:2610. 32147v1 Announce Type: cross Abstract: We propose a neural exploratory framework for variational problems over graphons, i.
By Taeyoung Kim, Jineon Baek, Joonkyung Lee, Hongseok Yang
arXiv:2606. 13912v1 Announce Type: cross Abstract: Neural-network quantum states (NQS) are a leading variational tool for quantum many-body physics, yet their optimization is fragile whenever the ground state carries a non-trivial sign or complex phase structure, a situation generic to gauge fields, broken time-reversal symmetry, and fermionic statistics.
By Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei
The paper proves that stochastic gradient descent with gradient clipping and additive Gaussian noise (SGD‑CN) converges almost surely under smoothness and bounded noise assumptions, given standard decaying step sizes. The analysis extends to momentum variants such as the stochastic heavy ball and Nesterov's accelerated gradient, showing that careful energy constructions yield similar guarantees. These results provide stronger theoretical foundations for understanding the pathwise behaviour of clipped stochastic gradient methods in both convex and nonconvex regimes.
By Amartya Mukherjee, Jun Liu
arXiv:2505.20817v3 Announce Type: replace-cross
Abstract: Gradient clipping is widely used in language-model training to control heavy-tailed gradient noise and can improve convergence guarantees ove...
By Taha El Bakkali El Kadi, Savelii Chezhegov, Aleksandr Beznosikov, Samuel Horv\'ath, Eduard Gorbunov
arXiv:2609.37425v1 Announce Type: cross
Abstract: Fix a target accuracy $\varepsilon$, a gradient-noise level $s$, and a horizon $N$. We wish to design algorithms which minimize the probability of ob...
By Bart P. G. van Parys
arXiv:2609.22342v1 Announce Type: cross
Abstract: Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failu...
By Yi-Ran Xue, Rui Wang, Baigeng Wang, Chenan Wei