313 citations · 714 across the 53 of their papers we have counts for
9 papers · 1 filter
Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function
Lei Wang, Xin Liu
In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of agents. The objective is an average of $…
DST: Data Selection and joint Training for Learning with Noisy Labels
Yi Wei, Xue Mei, Xin Liu +1
Training a deep neural network heavily relies on a large amount of training data with accurate annotations. To alleviate this problem, various methods have been proposed to annotat…
Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance
Chunjong Park, Morelle Arian, Xin Liu +5
Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night's sleep is important for jo…
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
Jianyong Sun, Xin Liu, Thomas Bäck +1
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorit…
Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio
Xin Liu, Kwok-Yan Lam, Feng Li +2
The explosive growth of bandwidth hungry Internet applications has led to the rapid development of new generation mobile network technologies that are expected to provide broadband…
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
Xin Liu, Yuang Li, Josh Fromm +4
Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning mode…
An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation
Maohan Liang, Ryan Wen Liu, Shichen Li +3
To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel tra…
OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space
Sungkwon An, Jeonghoon Kim, Myungjoo Kang +2
Novelty detection using deep generative models such as autoencoder, generative adversarial networks mostly takes image reconstruction error as novelty score function. However, imag…
Generalized Image Reconstruction over T-Algebra
Liang Liao, Xuechun Zhang, Xinqiang Wang +2
Principal Component Analysis (PCA) is well known for its capability of dimension reduction and data compression. However, when using PCA for compressing/reconstructing images, imag…