Zobrazeno 1 - 10
of 1 417
pro vyhledávání: '"Zhou, Mo"'
Classical neural ordinary differential equations (ODEs) are powerful tools for approximating the log-density functions in high-dimensional spaces along trajectories, where neural networks parameterize the velocity fields. This paper proposes a system
Externí odkaz:
http://arxiv.org/abs/2409.16471
The ability of learning useful features is one of the major advantages of neural networks. Although recent works show that neural network can operate in a neural tangent kernel (NTK) regime that does not allow feature learning, many works also demons
Externí odkaz:
http://arxiv.org/abs/2406.01766
Autor:
Zhou, Mo, Lu, Jianfeng
We propose an actor-critic framework to solve the time-continuous stochastic optimal control problem. A least square temporal difference method is applied to compute the value function for the critic. The policy gradient method is implemented as poli
Externí odkaz:
http://arxiv.org/abs/2402.17208
With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection. Yet, it turns into a harmful but inevitable training set bias upon test distributions that shift differently across
Externí odkaz:
http://arxiv.org/abs/2402.17207
Adversarial robustness often comes at the cost of degraded accuracy, impeding real-life applications of robust classification models. Training-based solutions for better trade-offs are limited by incompatibilities with already-trained high-performanc
Externí odkaz:
http://arxiv.org/abs/2402.02263
Autor:
Zhou, Mo, Fukuoka, Yoshimi, Mintz, Yonatan, Goldberg, Ken, Kaminsky, Philip, Flowers, Elena, Aswani, Anil
Publikováno v:
JMIR mHealth and uHealth, Vol 6, Iss 1, p e28 (2018)
BackgroundGrowing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. ObjectiveThe aim of this randomized controlled trial (RCT) was to evaluate the effica
Externí odkaz:
https://doaj.org/article/7a9a77d19bbe4e0c8f70130c36760886
Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these th
Externí odkaz:
http://arxiv.org/abs/2305.16310
Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces. While DPF shows great potential for unifying data generation of various modalities including images, videos, and 3D geometry, it does not
Externí odkaz:
http://arxiv.org/abs/2305.14674
Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation power. For example, arXiv:1904.06984 constructed a function that is eas
Externí odkaz:
http://arxiv.org/abs/2304.01063