Zobrazeno 1 - 10
of 5 278
pro vyhledávání: '"Luo, Zhi"'
Autor:
Zhang, Yushun, Chen, Congliang, Li, Ziniu, Ding, Tian, Wu, Chenwei, Ye, Yinyu, Luo, Zhi-Quan, Sun, Ruoyu
We propose Adam-mini, an optimizer that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint. Adam-mini reduces memory by cutting down the learning rate resources in Adam (i.e., $1/\sqrt{v}$). We find that $\geq$ 90%
Externí odkaz:
http://arxiv.org/abs/2406.16793
With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos according to simple and flexible conditioning is of particular interest. To this end, we propose a controllabl
Externí odkaz:
http://arxiv.org/abs/2406.05630
In this study, we investigate the dynamics of photons in black hole (BH) spacetimes within the framework of Horndeski theory, focusing on quasinormal modes (QNMs) and the optical appearances of BHs surrounded by thin accretion disks. We analyze photo
Externí odkaz:
http://arxiv.org/abs/2406.00265
In adversarial machine learning, neural networks suffer from a significant issue known as robust overfitting, where the robust test accuracy decreases over epochs (Rice et al., 2020). Recent research conducted by Xing et al.,2021; Xiao et al., 2022 h
Externí odkaz:
http://arxiv.org/abs/2405.01817
Autor:
Mao, Ting, Xu, Xufeng, Winkler, Pamina M., Siri, Cécilia, Poliukhina, Ekaterina, Silva, Paulo Jacob, Luo, Zhi, Ong, Quy, Katz, Alfredo-Alexander, Stellacci, Francesco
Despite being used for decades as stabilizers, amino acids (AAs) remain mysterious components of many medical and biological formulations. Hypotheses on their role vary ranging from hydrotropic to protein-specific effects (stabilization against misfo
Externí odkaz:
http://arxiv.org/abs/2404.11574
Autor:
Li, Yingru, Luo, Zhi-Quan
This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian regret bound for RL with function approximation; and refine the Bayes
Externí odkaz:
http://arxiv.org/abs/2403.11175
SGD performs worse than Adam by a significant margin on Transformers, but the reason remains unclear. In this work, we provide an explanation through the lens of Hessian: (i) Transformers are "heterogeneous": the Hessian spectrum across parameter blo
Externí odkaz:
http://arxiv.org/abs/2402.16788
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this pap
Externí odkaz:
http://arxiv.org/abs/2402.16274
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making. We introduce Thompson
Externí odkaz:
http://arxiv.org/abs/2402.09456
We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL. HyperAgent allows for the efficient incremental approximation of posteriors associated with an optimal action-value function ($Q^\
Externí odkaz:
http://arxiv.org/abs/2402.10228