With the wide adoption of mobile devices, today's location tracking systems such as satellites, cellular base stations and wireless access points are continuously producing tremendous amounts of location data of moving objects. The ability to discover moving objects that travel together, i.e., traveling companions, from their trajectories is desired by many applications such as intelligent transportation systems and location-based services. Existing algorithms are either based on pattern mining methods that define a particular pattern of traveling companions or based on representation learning methods that learn similar representations for similar trajectories. The former methods suffer from the pairwise point-matching problem and the latter often ignore the temporal proximity between trajectories. In this work, we propose a generic deep representation learning model using autoencoders, namely, ATTN-MEAN, for the discovery of traveling companions. ATTN-MEAN collectively injects spatial and temporal information into its input embeddings using skip-gram, positional encoding techniques, respectively. Besides, our model further encourages trajectories to learn from their neighbours by leveraging the Sort-Tile-Recursive algorithm, mean operation and global attention mechanism. After obtaining the representations from the encoders, we run DBSCAN to cluster the representations to find travelling companion. The corresponding trajectories in the same cluster are considered as traveling companions. Experimental results suggest that ATTN-MEAN performs better than the state-of-the-art algorithms on finding traveling companions.
The Use of Autoencoders for Discovering Patient Phenotypes
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
我们使用自动编码器来创建潜在患者表型的低维嵌入，我们假设这些表型是决定不同患者对不同干预措施反应的决定因素。我们比较了自编码器的性能，以固定长度的级联时间步序列作为输入与经常性的序列到序列的自编码器。我们对来自Beth Israel Deaconess Hospital的最新MIMIC III数据集的约35，500名患者进行了评估。
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
The Swiss AI Lab, IDSIA, USI & SUPSI（瑞士AI实验室，IDSIA，USI与SUPSI）；Center for Brain Science, Harvard University（哈佛大学脑科学中心）；AI Initiative, KAUST（阿卜杜拉国王科技大学AI计划）
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
Shaanxi Normal University（陕西师范大学）；University of Georgia（佐治亚大学）；Northwestern Polytechnical University（西北工业大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
State Key Laboratory of Digital Medical Engineering, Southeast University（数字医疗工程国家重点实验室，东南大学）；School of Instrument Science and Engineering, Southeast University（仪器科学与工程学院，东南大学）；School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）
Discovering and decoding latent mean-field structure with variational autoencoders
Department of Physics and the James Franck Institute, University of Chicago（芝加哥大学物理系及詹姆斯·弗兰克研究所）；CuspAI；AMLab, University of Amsterdam（阿姆斯特丹大学AMLab）；Leinweber Institute for Theoretical Physics（莱因韦伯理论物理研究所）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）；CERN（欧洲核子研究组织）
我们应用逆向学习异常检测（ALAD）算法在大型强子对撞机的质子-质子碰撞中发现新的物理过程的问题。基于ALAD的异常检测与变分自编码器达到的性能相匹配，在某些情况下有很大的改进。在4.4 fb-1的8 TeV CMS开放数据上训练ALAD算法，我们展示了数据驱动的异常检测和表征如何在现实生活中工作，通过识别LHC的t-tbar实验签名的主要特征来重新发现顶夸克。
Center for Computing Research, Sandia National Laboratories（桑迪亚国家实验室计算研究中心）；Applied Information Sciences Center, Sandia National Laboratories（桑迪亚国家实验室应用信息科学中心）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）
Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) have been proposed to explore DPMs for representation learning via autoencoding. Their key idea is to jointly train an encoder for discovering meaningful representations from images and a conditional DPM as the decoder for reconstructing images. Considering that training DPMs from scratch will take a long time and there have existed numerous pre-trained DPMs, we propose \textbf{P}re-trained \textbf{D}PM \textbf{A}uto\textbf{E}ncoding (\textbf…
Shandong Provincial Key Laboratory of Network Based Intelligent Computing（山东省网络智能计算重点实验室）；University of Jinan（济南大学）；Jinan, 250022, China（济南市，250022，中国）
"Task-relevant autoencoding" enhances machine learning for human neuroscience
University of California, Irvine（加州大学欧文分校）；Université de Montréal（蒙特利尔大学）；Centre de recherche de l’institut universitaire en santé mentale de Montréal（蒙特利尔大学精神健康研究所研究中心）；ATR Computational Neuroscience Laboratories（ATR计算神经科学实验室）；University of Florida（佛罗里达大学）；University of California Los Angeles（加州大学洛杉矶分校）；RIKEN Center for Brain Science（理化学研究所脑科学中心）
