Spatial Classification With Limited Observations Based On Physics-Aware Structural Constraint

Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. Existing research mostly focuses on addressing incomplete or missing data, e.g., data cleaning and imputation, classification models that allow for missing feature values or model missing features as hidden variables in the EM algorithm. These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations. To address this issue, we recently proposed a new approach that incorporates physics-aware structural constraint into the model representation. Our approach assumes that a spatial contextual feature is observed for all sample locations and establishes spatial structural constraint from the underlying spatial contextual feature map. We design efficient algorithms for model parameter learning and class inference. This paper extends our recent approach by allowing feature values of samples in each class to follow a multi-modal distribution. We propose learning algorithms for the extended model with multi-modal distribution. Evaluations on real-world hydrological applications show that our approach significantly outperforms baseline methods in classification accuracy, and the multi-modal extension is more robust than our early single-modal version especially when feature distribution in training samples is multi-modal. Computational experiments show that the proposed solution is computationally efficient on large datasets

MMCPP: A MULTI-MODAL CONTRASTIVE PRE-TRAINING MODEL FOR PLACE REPRESENTATION BASED ON THE SPATIO-TEMPORAL FRAMEWORK

The concept of "place" is crucial for understanding geographical environments from a human perspective. Place representation learning involves converting places into numerical low-dimensional dense vectors and is a fundamental procedure for artificial intelligence in geography (GeoAI). However, most studies ignore multi-level distance constraints and spatial proximity interactions that enable behavioral interactions between places. Furthermore, representing the temporal characteristics of these interactions in trajectory sequences poses a challenge for natural language processing and other field techniques. In addition, most existing methods rely on all modalities from inputs as they use joint training to integrate multiple modalities. To address these issues, we propose a Multi-Modal Contrastive Pre-training model for Place representation (MMCPP). Our model consists of three encoders that capture corresponding place attributes across different modalities, including point of interests (POIs), images, and trajectories. The trajectory encoder, named RodtFormer, takes fine-grained spatio-temporal trajectories as input and leverages self-attention with rotary temporal interval position embedding to simulate dynamic spatial and behavioral proximity interactions between places. By using a coordinated pre-training framework, MMCPP independently encodes place representations across different modalities and improves model reusability. We verify the effectiveness of our model on a taxi trajectory dataset using the location prediction task at next n seconds, including 30 seconds(s), 180(s), 300(s). Our results demonstrate that compared to existing embedding methods, our model is capable of learning higher-quality position representations during pre-training, leading to improved performance on downstream tasks

Generative adversarial networks for sequential learning

Generative modelling aims to learn the data generating mechanism from observations without supervision. It is a desirable and natural approach for learning unlabelled data which is easily accessible. Deep generative models refer to a class of generative models combined with the usage of deep learning techniques, taking advantage of the intuitive principles of generative models as well as the expressiveness and flexibility of neural networks. The applications of generative modelling include image, audio, and video synthesis, text summarisation and translation, and so on. The methods developed in this thesis particularly emphasise on domains involving data of sequential nature, such as video generation and prediction, weather forecasting, and dynamic 3D reconstruction. Firstly, we introduce a new adversarial algorithm for training generative models suitable for sequential data. This algorithm is built on the theory of Causal Optimal Transport (COT) which constrains the transport plans to respect the temporal dependencies exhibited in the data. Secondly, the algorithm is extended to learn conditional sequences, that is, how a sequence is likely to evolve given the observation of its past evolution. Meanwhile, we work with the modified empirical measures to guarantee the convergence of the COT distance when the sequences do not overlap at any time step. Thirdly, we show that state-of-the-art results in the complex spatio-temporal modelling using GANs can be further improved by leveraging prior knowledge in the spatial-temporal correlation in the domain of weather forecasting. Finally, we demonstrate how deep generative models can be adopted to address a classical statistical problem of conditional independence testing. A class of classic approaches for such a task requires computing a test statistic using samples drawn from two unknown conditional distributions. We therefore present a double GANs framework to learn two generative models that approximate both conditional distributions. The success of this approach sheds light on how certain challenging statistical problems can benefit from the adequate learning results as well as the efficient sampling procedure of deep generative models

Unifying Inter-Region Autocorrelation and Intra-Region Structures for Spatial Embedding via Collective Adversarial Learning

Unsupervised spatial representation learning aims to automatically identify effective features of geographic entities (i.e., regions) from unlabeled yet structural geographical data. Existing network embedding methods can partially address the problem by: (1) regarding a region as a node in order to reformulate the problem into node embedding; (2) regarding a region as a graph in order to reformulate the problem into graph embedding. However, these studies can be improved by preserving (1) intra-region geographic structures, which are represented by multiple spatial graphs, leading to a reformulation of collective learning from relational graphs; (2) inter-region spatial autocorrelations, which are represented by pairwise graph regularization, leading to a reformulation of adversarial learning. Moreover, field data in real systems are usually lack of labels, an unsupervised fashion helps practical deployments. Along these lines, we develop an unsupervised Collective Graph-regularized dual-Adversarial Learning (CGAL) framework for multi-view graph representation learning and also a Graph-regularized dual-Adversarial Learning (GAL) framework for single-view graph representation learning. Finally, our experimental results demonstrate the enhanced effectiveness of our method

Learning with Attributed Networks: Algorithms and Applications

abstract: Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges. My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Learning visual representations with neural networks for video captioning and image generation

La recherche sur les réseaux de neurones a permis de réaliser de larges progrès durant la dernière décennie. Non seulement les réseaux de neurones ont été appliqués avec succès pour résoudre des problèmes de plus en plus complexes; mais ils sont aussi devenus l’approche dominante dans les domaines où ils ont été testés tels que la compréhension du langage, les agents jouant à des jeux de manière automatique ou encore la vision par ordinateur, grâce à leurs capacités calculatoires et leurs efficacités statistiques. La présente thèse étudie les réseaux de neurones appliqués à des problèmes en vision par ordinateur, où les représentations sémantiques abstraites jouent un rôle fondamental. Nous démontrerons, à la fois par la théorie et par l’expérimentation, la capacité des réseaux de neurones à apprendre de telles représentations à partir de données, avec ou sans supervision. Le contenu de la thèse est divisé en deux parties. La première partie étudie les réseaux de neurones appliqués à la description de vidéo en langage naturel, nécessitant l’apprentissage de représentation visuelle. Le premier modèle proposé permet d’avoir une attention dynamique sur les différentes trames de la vidéo lors de la génération de la description textuelle pour de courtes vidéos. Ce modèle est ensuite amélioré par l’introduction d’une opération de convolution récurrente. Par la suite, la dernière section de cette partie identifie un problème fondamental dans la description de vidéo en langage naturel et propose un nouveau type de métrique d’évaluation qui peut être utilisé empiriquement comme un oracle afin d’analyser les performances de modèles concernant cette tâche. La deuxième partie se concentre sur l’apprentissage non-supervisé et étudie une famille de modèles capables de générer des images. En particulier, l’accent est mis sur les “Neural Autoregressive Density Estimators (NADEs), une famille de modèles probabilistes pour les images naturelles. Ce travail met tout d’abord en évidence une connection entre les modèles NADEs et les réseaux stochastiques génératifs (GSN). De plus, une amélioration des modèles NADEs standards est proposée. Dénommés NADEs itératifs, cette amélioration introduit plusieurs itérations lors de l’inférence du modèle NADEs tout en préservant son nombre de paramètres. Débutant par une revue chronologique, ce travail se termine par un résumé des récents développements en lien avec les contributions présentées dans les deux parties principales, concernant les problèmes d’apprentissage de représentation sémantiques pour les images et les vidéos. De prometteuses directions de recherche sont envisagées.The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real- world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networks’ superiority in computational efficiency and statistical capacity. This thesis applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the thesis is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed iterative NADE. With a historical view at the beginning, this work ends with a summary of recent development for work discussed in the first two parts around the central topic of learning visual representations for images and videos. A bright future is envisioned at the end

Machine learning and the physical sciences

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges