Zobrazeno 1 - 10
of 766
pro vyhledávání: '"LI, Lingxiao"'
Autor:
Li, Lingxiao
Modern machine learning applications, such as generative modeling and probabilistic inference, demand a new generation of methodologies for optimizing over the space of probability distributions, where the optimization variable represents a weighted
Externí odkaz:
https://hdl.handle.net/1721.1/156585
Radar-based contactless cardiac monitoring has become a popular research direction recently, but the fine-grained electrocardiogram (ECG) signal is still hard to reconstruct from millimeter-wave radar signal. The key obstacle is to decouple the cardi
Externí odkaz:
http://arxiv.org/abs/2408.01672
Modern compression methods can summarize a target distribution $\mathbb{P}$ more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chain converging quickly to $\mathbb{P}$. We introduce a new suite of compr
Externí odkaz:
http://arxiv.org/abs/2404.12290
Linguistic steganography (LS) tasks aim to generate steganographic text (stego) based on secret. Only authorized receivers can perceive and extract secrets, thereby protecting privacy. However, existing generative LS schemes often do not consider the
Externí odkaz:
http://arxiv.org/abs/2401.15656
Solving optimization problems with high performance is the target of existing works of Quantum Approximate Optimization Algorithm (QAOA). With this intention, we propose an advanced QAOA based on incremental learning, where the training trajectory is
Externí odkaz:
http://arxiv.org/abs/2311.02302
Linguistic steganalysis (LS) tasks aim to detect whether a text contains secret information. Existing LS methods focus on the deep-learning model design and they achieve excellent results in ideal data. However, they overlook the unique user characte
Externí odkaz:
http://arxiv.org/abs/2311.01775
Autor:
Hao, Wei, Wang, Zixi, Hong, Lauren, Li, Lingxiao, Karayanni, Nader, Mao, Chengzhi, Yang, Junfeng, Cidon, Asaf
ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation. However, once the models are deployed, it is hard for ML operators to track their accuracy, which can degrade unpredictably (e.g., due to data
Externí odkaz:
http://arxiv.org/abs/2305.07772
Publikováno v:
Anti-Corrosion Methods and Materials, 2024, Vol. 71, Issue 4, pp. 417-427.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ACMM-02-2024-2967
We consider (robust) inference in the context of a factor model for tensor-valued sequences. We study the consistency of the estimated common factors and loadings space when using estimators based on minimising quadratic loss functions. Building on t
Externí odkaz:
http://arxiv.org/abs/2303.18163
Factor models have been widely used in economics and finance. However, the heavy-tailed nature of macroeconomic and financial data is often neglected in the existing literature. To address this issue and achieve robustness, we propose an approach to
Externí odkaz:
http://arxiv.org/abs/2303.02817