Zobrazeno 1 - 10
of 545
pro vyhledávání: '"Ge Rong"'
Publikováno v:
Energy and Built Environment, Vol 6, Iss 1, Pp 57-65 (2025)
The high-level biosafety laboratory is not only the basic support for infectious disease prevention and control, but also interrelated with key areas such as environmental security and social security, which has attracted increasing attention. A good
Externí odkaz:
https://doaj.org/article/0bd8a8b6d7dc4882b35a9adf9cc757c9
Autor:
Xie, Roy, Wang, Junlin, Huang, Ruomin, Zhang, Minxing, Ge, Rong, Pei, Jian, Gong, Neil Zhenqiang, Dhingra, Bhuwan
The rapid scaling of large language models (LLMs) has raised concerns about the transparency and fair use of the pretraining data used for training them. Detecting such content is challenging due to the scale of the data and limited exposure of each
Externí odkaz:
http://arxiv.org/abs/2406.15968
Autor:
Chidambaram, Muthu, Ge, Rong
A machine learning model is calibrated if its predicted probability for an outcome matches the observed frequency for that outcome conditional on the model prediction. This property has become increasingly important as the impact of machine learning
Externí odkaz:
http://arxiv.org/abs/2406.04068
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
Discrete GPU accelerators, while providing massive computing power for supercomputers and data centers, have their separate memory domain. Explicit memory management across device and host domains in programming is tedious and error-prone. To improve
Externí odkaz:
http://arxiv.org/abs/2405.06811
Autor:
Li, Shuyao, Cheng, Yu, Diakonikolas, Ilias, Diakonikolas, Jelena, Ge, Rong, Wright, Stephen J.
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning. However, this problem is poorly understood in the presence of outli
Externí odkaz:
http://arxiv.org/abs/2403.10547
Recent research has demonstrated that transformers, particularly linear attention models, implicitly execute gradient-descent-like algorithms on data provided in-context during their forward inference step. However, their capability in handling more
Externí odkaz:
http://arxiv.org/abs/2402.14180
Autor:
Chen, Ziang, Ge, Rong
In this work, we study the mean-field flow for learning subspace-sparse polynomials using stochastic gradient descent and two-layer neural networks, where the input distribution is standard Gaussian and the output only depends on the projection of th
Externí odkaz:
http://arxiv.org/abs/2402.08948
Autor:
Chidambaram, Muthu, Ge, Rong
Data augmentation has been pivotal in successfully training deep learning models on classification tasks over the past decade. An important subclass of data augmentation techniques - which includes both label smoothing and Mixup - involves modifying
Externí odkaz:
http://arxiv.org/abs/2402.06855
Autor:
Randall, Thomas, Koo, Jaehoon, Videau, Brice, Kruse, Michael, Wu, Xingfu, Hovland, Paul, Hall, Mary, Ge, Rong, Balaprakash, Prasanna
Publikováno v:
Proceedings of the 37th International Conference on Supercomputing (2023) 37-49
As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increased complexity of these HPC systems and application tuning, empirical pe
Externí odkaz:
http://arxiv.org/abs/2401.04669