Dynamic graph neural networks have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discrete learning which results in temporal information loss or continuous learning that involves heavy computation. In this work, we proposed a novel dynamic graph neural network, Sparse-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding the use of snapshots which causes information loss, it also achieves a finer time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through both structural neighborhoods and temporal dynamics. Since the fully-connected attention conjunction is simplified, the computation cost is far lower than the current state-of-the-arts. Link prediction experiments are conducted on both continuous and discrete graph datasets. Through comparing with several state-of-the-art graph embedding baselines, the experimental results demonstrate that Sparse-Dyn has a faster inference speed while having competitive performance.
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that representations can be disentangled if all but a few factors in the environment stay constant at any point in time. As a result, algorithms proposed for this problem have only been tested on carefully constructed datasets with this exact property, leaving it unclear whether they will transfer to natural scenes. Here we provide evidence that objects in segmented natural movies undergo transitions that are typically small in ma…
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. In this work, we propose a block sparse Bayesian learning framework which models the temporal correlations. In this framework we derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presen…
University of Sheffield（谢菲尔德大学）；University of Oxford（牛津大学）
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. Th…
Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data
MOX, Department of Mathematics, Politecnico di Milano（数学系，米兰理工学院MOX部门）；Modeling and Simulation of Socio-Technical Systems, Fondazione Bruno Kessler（社会技术系统建模与仿真，布鲁诺·凯瑟勒基金会）
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Memphis（孟菲斯大学）
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse approximation with improved quality over current rejection-based sparse methods. We derive the predictive value function posterior and use the moments to obtain a new algorithm for model-free policy evaluation, SPGP-SARSA. With simple changes, we show SPGP-SARSA can be reduced to a …
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Université Mohammed VI Polytechnique（穆罕默德六世理工大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU)（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）
Traffic state prediction in a transportation network is paramount for effective traffic operations and management, as well as informed user and system-level decision-making. However, long-term traffic prediction (beyond 30 minutes into the future) remains challenging in current research. In this work, we integrate the spatio-temporal dependencies in the transportation network from network modeling, together with the graph convolutional network (GCN) and graph attention network (GAT). To further tackle the dramatic computation and memory cost caused by the giant model size (i.e., number of weig…
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Institute for Human-Centered AI, Helmholtz Computational Health Center（亥姆霍兹计算健康中心以人为本人工智能研究所）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）
Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K in…
Massachusetts Institute of Technology（麻省理工学院）；University of Florida（佛罗里达大学）；Northeastern University（东北大学）
Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncert…
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Meta；Center of Excellence for Molecular Prediction of IBD (PREDICT)（分子预测IBD中心（PREDICT））；Department of Clinical Medicine, Aalborg University（临床医学系，奥胡斯大学）；Department of Gastroenterology & Hepatology, Aalborg University Hospital（消化内科与肝病科，奥胡斯大学医院）；University of Copenhagen（哥本哈根大学）；Imperial College London（伦敦帝国理工学院）；Department of Biomedical Engineering, Tel-Aviv University（生物医学工程系，特拉维夫大学）；Sagol School of Neuroscience, Tel-Aviv University（神经科学学院，特拉维夫大学）
Embedding interpretable $\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging
Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Germany（医学生物度量与统计研究所（IMBI）、医学学院与医疗中心、弗赖堡大学）；Institute of Physiology I, Faculty of Medicine, University of Freiburg, Germany（医学生理学研究所I、医学学院、弗赖堡大学）；Department of Computer Science, University of Freiburg, Germany（计算机科学系、弗赖堡大学）；Freiburg Center for Data Analysis, Modeling and AI (FDMAI), University of Freiburg, Germany（弗赖堡数据分析、建模与人工智能中心（FDMAI）、弗赖堡大学）；Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Germany（整合生物信号研究中心（CIBSS）、弗赖堡大学）
Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance, and urban traffic monitoring. Existing anomaly detection methods are most suited for point anomalies in sequence data and cannot deal with temporal and spatial dependencies that arise in spatiotemporal data. In recent years, tensor-based methods have been proposed for anomaly detection to address this problem. These methods rely on conventional tensor decomposition models, not taking the structure of the anomalies into account, and are …
Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversarial attacks are threatening the functionality of GNNs. Although numerous works have been proposed to defend adversarial attacks from various perspectives, most of them can be robust against the attacks only on specific scenarios. To address this shortage of robust generalization, we propose to defend the adversarial attacks on GNN through applying the Spatio-Temporal sparsification (called ST-Sparse) on the GNN hidd…
Arizona State University（亚利桑那州立大学）；Texas Tech University（得克萨斯理工大学）
Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i.e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation. Detecting and identifying of cardiac cells that produce abnormal electrical impulses in such nonlinear dynamic systems are important for efficient treatment and planning. To model the nonlinear dynamics, simulation has been widely used in both cardiac research and clinical study to investigate cardiac disease mechanisms and develop new treatment designs. …
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Tata Consultancy Services（塔塔咨询服务公司）；Birla Institute of Technology and Science, Goa（果阿贝拉理工学院）；University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；Indian Institute of Technology, Bombay（印度理工学院孟买分校）
Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
Harvard School of Engineering and Applied Science（哈佛工程与应用科学学院）；Kempner Institute for the Study of Natural & Artificial Intelligence（自然与人工智能研究学院）；Harvard Business School（哈佛商学院）
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney（澳大利亚人工智能研究所（AAII），悉尼科技大学工程与信息技术学院）
Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently. However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.), but also because of the temporal and spatial sparsity in the dataset. For a…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for biologically plausible spiking neural networks (SNNs). Datasets for traditional few-shot learning domains provide few amounts of temporal information. and the absence of neuromorphic datasets has hindered the development of few-shot learning for SNNs. Here, to the best of our knowledge, we provide the first neuromorphic dataset for few-shot learning…
REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；Bern University of Applied Sciences（伯尔尼应用科学大学）；University of Fribourg（弗里堡大学）
Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications（南京邮电大学江苏省大数据安全与智能处理重点实验室）；National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University（北京工商大学农产品质量安全追溯技术及应用国家工程实验室）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院江苏省社会安全图像与视频理解重点实验室）
With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicati…
Tianjin University（天津大学）；Netease Fuxi AI Lab（网易伏羲人工智能实验室）；Nanjing University（南京大学）
Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challenging when rewards are sparse and delayed over long trajectories. In this paper, we study hierarchical deep MARL in cooperative multiagent problems with sparse and delayed reward. With temporal abstraction, we decompose the problem into a hierarchy of different time scales and investigate how agents can learn high-level coordination based on the independent skills learned at the low level. Three hierarchical deep MARL …
Joint sparse coding and temporal dynamics support context reconfiguration
Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China（脑启发计算研究中心（CBICR）、精密仪器系、清华大学、北京、中国）；Optical Memory National Engineering Research Center, Tsinghua University, Beijing, China（光学存储国家级工程研究中心、清华大学、北京、中国）；IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China（IDG/麦戈文脑研究 institute、清华大学、北京、中国）；Guangdong Institute of Intelligence Science and Technology, Hengqin, China（广东智能科学与技术研究院、横琴、中国）；Sainsbury Wellcome Centre, University College London, London, UK（桑斯伯里·惠康中心、伦敦大学学院、伦敦、英国）；Max Planck Institute for Biological Intelligence, Martinsried, Germany（马克斯·普朗克生物智能研究所、马尔特里德、德国）
A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs …
Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
Advanced Concepts Team European Space Agency（欧洲航天局高级概念团队）；Department of Physics Università degli Studi di Roma Sapienza（罗马大学物理系）；Department of Engineering Science University of Oxford（牛津大学工程科学系）；Department of Information and Computer Science University of Hawai’i at Mānoa（夏威夷大学信息与计算机科学系）；Free Flight Research Lab（自由飞行研究实验室）；NASA Headquarters（美国国家航空航天局总部）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of New Hampshire（新罕布什尔大学）；Department of Computer Science University of Oxford, UK（牛津大学计算机科学系）；Department of Computer Science University of York & King’s College London（约克大学计算机科学系及伦敦国王学院计算机科学系）
University of Cambridge（剑桥大学）；ETH Zürich（苏黎世联邦理工学院）；Warwick University（华威大学）
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the de…
University of Maryland, College Park（马里兰大学帕克分校）；Amazon.com, Inc.（亚马逊公司）
We present FlexNeRF, a method for photorealistic freeviewpoint rendering of humans in motion from monocular videos. Our approach works well with sparse views, which is a challenging scenario when the subject is exhibiting fast/complex motions. We propose a novel approach which jointly optimizes a canonical time and pose configuration, with a pose-dependent motion field and pose-independent temporal deformations complementing each other. Thanks to our novel temporal and cyclic consistency constraints along with additional losses on intermediate representation such as segmentation, our approach …
Affective computing is very important in the relationship between man and machine. In this paper, a system for speech emotion recognition (SER) based on speech signal is proposed, which uses new techniques in different stages of processing. The system consists of three stages: feature extraction, feature selection, and finally feature classification. In the first stage, a complex set of long-term statistics features is extracted from both the speech signal and the glottal-waveform signal using a combination of new and diverse features such as prosodic, spectral, and spectro-temporal features. …
