Brain network discovery aims to find nodes and edges from the spatio-temporal signals obtained by neuroimaging data, such as fMRI scans of human brains. Existing methods tend to derive representative or average brain networks, assuming observed signals are generated by only a single brain activity state. However, the human brain usually involves multiple activity states, which jointly determine the brain activities. The brain regions and their connectivity usually exhibit intricate patterns that are difficult to capture with only a single-state network. Recent studies find that brain parcellation and connectivity change according to the brain activity state. We refer to such brain networks as multi-state, and this mixture can help us understand human behavior. Thus, compared to a single-state network, a multi-state network can prevent us from losing crucial information of cognitive brain network. To achieve this, we propose a new model called MNGL (Multi-state Network Graphical Lasso), which successfully models multi-state brain networks by combining CGL (coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both synthetic and real world ADHD-200 fMRI datasets, we demonstrate that MNGL outperforms recent state-of-the-art alternatives by discovering more explanatory and realistic results.

Sparse inverse covariance estimation (i.e., edge detection) is an important research problem in recent years, where the goal is to discover the direct connections between a set of nodes in a networked system based upon the observed node activities. Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities are generated from a single Gaussian distribution (i.e., one graph). However, this assumption is too strong for many real-world applications. In many real-world applications (e.g., brain networks), the node activities usually exhibit much more complex patterns that are difficult to be captured by one single Gaussian distribution. In this work, we are inspired by Latent Dirichlet Allocation (LDA) [4] and consider modeling the edge detection problem as estimating a mixture of multiple Gaussian distributions, where each corresponds to a separate sub-network. To address this problem, we propose a novel model called Gaussian Mixture Graphical Lasso (MGL). It learns the proportions of signals generated by each mixture component and their parameters iteratively via an EM framework. To obtain more interpretable networks, MGL imposes a special regularization, called Mutual Exclusivity Regularization (MER), to minimize the overlap between different sub-networks. MER also addresses the common issues in read-world data sets, i.e., noisy observations and small sample size. Through the extensive experiments on synthetic and real brain data sets, the results demonstrate that MGL can effectively discover multiple connectivity structures from the observed node activities.

Attention-based image classification has gained increasing popularity in recent years. State-of-the-art methods for attention-based classification typically require a large training set and operate under the assumption that the label of an image depends solely on a single object (i.e. region of interest) in the image. However, in many real-world applications (e.g. medical imaging), it is very expensive to collect a large training set. Moreover, the label of each image is usually determined jointly by multiple regions of interest (ROIs). Fortunately, for such applications, it is often possible to collect the locations of the ROIs in each training image. In this paper, we study the problem of guided multi-attention classification, the goal of which is to achieve high accuracy under the dual constraints of (1) small sample size, and (2) multiple ROIs for each image. We propose a model, called Guided Attention Recurrent Network (GARN), for multi-attention classification. Different from existing attention-based methods, GARN utilizes guidance information regarding multiple ROIs thus allowing it to work well even when sample size is small. Empirical studies on three different visual tasks show that our guided attention approach can effectively boost model performance for multi-attention image classification.

Neuroimaging data typically undergoes several preprocessing steps before further analysis and mining can be done. Affine image registration is one of the important tasks during preprocessing. Recently, several image registration methods which are based on Convolutional Neural Networks have been proposed. However, due to the high computational and memory requirements of CNNs, these methods cannot be used in real-time for large neuroimaging data like fMRI. In this paper, we propose a Dual-Attention Recurrent Network (DRN) which uses a hard attention mechanism to allow the model to focus on small, but task-relevant, parts of the input image – thus reducing computational and memory costs. Furthermore, DRN naturally supports inhomogeneity between the raw input image (e.g., functional MRI) and the image we want to align it to (e.g., anatomical MRI) so it can be applied to harder registration tasks such as fMRI coregistration and normalization. Extensive experiments on two different datasets demonstrate that DRN significantly reduces the computational and memory costs compared with other neural network-based methods without sacrificing the quality of image registration.

Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.

In brain network discovery, researchers are interested in discovering brain regions (nodes) and functional connections (edges) between these regions from fMRI scan of human brain. Some recent works propose coherent models to address both of these sub-tasks. However, these approaches either suffer from mathematical inconsistency or fail to distinguish direct connections and indirect connections between the nodes. In this paper, we study the problem of collective discovery of coherent brain regions and direct connections between these regions. Each node of the brain network represents a brain region, i.e., a set of voxels in fMRI with coherent activities. Each edge denotes a direct dependency between two nodes. The discovered brain network represents a Gaussian graphical model that encodes conditional independence between the activities of different brain regions. We propose a novel model, called CGLasso, which combines Graphical Lasso (GLasso) and orthogonal non-negative matrix tri-factorization (ONMtF), to perform nodes discovery and edge detection simultaneously. We perform experiments on synthetic datasets with ground-truth. The results show that the proposed method performs better than the compared baselines in terms of four quantitative metrics. Besides, we also apply the proposed method and other baselines on the real ADHD-200 fMRI dataset. The results demonstrate that our method produces more meaningful networks comparing with other baseline methods.

Influence maximization (IM) targets at maximizing the number of users being aware of a product by finding a set of seed users to expose in a social network. Previous IM models mainly focus on optimizing the spread of product consumption, which assumes that all users are potential customers and more exposures lead to better profit. However, in the real-world scenario, some people may not like the product and may express negative opinions after consuming, which damage the product reputation and harm the long-term profit. Only a portion of users in the social network, called the target user, is the potential customer that likes the product and will spread positive opinion. In this paper, we consider a problem called AcTive Opinion Maximization (ATOM), where the goal is to find a set of seed users to maximize the overall opinion spread toward a target product in a multi-round campaign. Different from previous works, we do not assume the user opinion is known before consumption, but should be derived from user preference data. The ATOM problem has essential applications in viral marketing, such as reputation building and precision advertising. Given its significance, ATOM problem is profoundly challenging due to the hardness of estimating user opinion in a multi-round campaign. Moreover, the process of opinion estimation and influence propagation intertwine with each other, which requires the model to consider the two components collectively. We propose an active learning framework called CONE (aCtive OpinioN Estimator) to address above challenges. Experimental results on two real-world datasets demonstrate that CONE improves the total opinion spread in a social network.

Recommender systems have attracted much attention in last decades, which can help the users explore new items in many applications. As a popular technique in recommender systems, item recommendation works by recommending items to users based on their historical interactions. Conventional item recommendation methods usually assume that users and items are stationary, which is not always the case in real-world applications. Many time-aware item recommendation models have been proposed to take the temporal effects into the considerations based on the absolute time stamps associated with observed interactions. We show that using absolute time to model temporal effects can be limited in some circumstances. In this work, we propose to model the temporal dynamics of both users and items in item recommendation based on their life cycles. This problem is very challenging to solve since the users and items can co-evolve in their life cycles and the sparseness of the data become more severe when we consider the life cycles of both users and items. A novel time-aware item recommendation model called BiCycle is proposed to address these challenges. BiCycle is designed based on two important observations: 1) correlated users or items usually share similar patterns in the similar stages of their life cycles. 2) user preferences and item characters can evolve gradually over different stages of their life cycles. Extensive experiments conducted on three real-world datasets demonstrate the proposed approach can significantly improve the performance of recommendation tasks by considering the inner life cycles of both users and items.

Singular value decomposition (SVD) has been used widely in the literature to recover the missing entries of a matrix. The basic principle in such methods is to assume that the correlated data is distributed with a low-rank structure. The knowledge of the low-rank structure is then used to predict the missing entries. SVD is based on the assumption that the data (user ratings) are distributed on a linear hyperplane. This is not always the case, and the data could often be distributed on a nonlinear hyperplane. Therefore, in this paper, we explore the methodology of kernel feature extraction to complement off-the-shelf methods for improving their accuracy. The extracted features can be used to enhance a variety of existing methods such as biased matrix factorization and SVD++. We present experimental results illustrating the effectiveness of using this approach.

Brain network discovery has attracted much attention in recent years, which aims at inferring a set of cohesive regions (i.e., the network nodes) and the connectivity between these regions (i.e., the network edges) in brain from neuroimaging data (e.g., fMRI, PET scans). Previous methods on brain network discovery mainly focus on either estimating the connectivity based on predefined brain regions, or inferring the brain regions and connectivity independently. However, the tasks of discovering brain regions and their connectivity are highly related to each other and should be discovered collectively, instead of independently. In this work, we propose a coherent data-driven method called SGGL (Spectral Group Graphical Lasso) to derive the nodes and edges of a brain network simultaneously. We propose a screening strategy to reduce the time cost of solving the corresponding optimization problem. Extensive experiments are performed on both synthetic data and real data from ADHD-200 project. The results demonstrate the effectiveness of the proposed method.

The analysis of brain imaging data has attracted much attention recently. A popular analysis is to discover a network representation of brain from the neuroimaging data, where each node denotes a brain region and each edge represents a functional association or structural connection between two brain regions. Motivated by the multi-subject and multi-collection settings in neuroimaging studies, in this paper, we consider brain network discovery under two novel settings: 1) unified setting: Given a collection of subjects, discover a single network that is good for all subjects. 2) contrasting setting: Given two collections of subjects, discover a single network that best discriminates two collections. We show that the existing formulation of graphical Lasso (GLasso) cannot address above problems properly. Two novel models, UGLasso (Unified Graphical Lasso) and CGLasso(Contrasting Graphical Lasso), are proposed to address these two problems respectively. We evaluate our methods on synthetic data and two real-world functional magnetic resonance imaging (fMRI) datasets. Empirical results demonstrate the effectiveness of the proposed methods.

Sparse inverse covariance estimation has attracted lots of interests since it can recover the structure of the underlying Gaussian graphical model. This is a useful tool to demonstrate the connections among objects (nodes). Previous works on sparse inverse covariance estimation mainly focus on learning one single type of connections from the observed activities with a lasso, group lasso or tree-structure penalty. However, in many real-world applications, the observed activities on the nodes can be related to multiple types of connections. In this paper, we consider the problem of learning heterogeneous connectivities from the observed activities by incorporating meta paths extracted from a heterogeneous information network (HIN), an information network with multiple types of nodes and links, into the conventional graphical lasso framework. We aim at extracting the strongest type of relation between any pairs of entities and ignoring other minor relations. Specially, we introduce two novel kinds of constraints: meta path constraints and exclusive constraints, which ensure the unique type of relation among a pair of objects. This problem is highly challenging due to the non-convex optimization. We proposed a method based upon the alternating direction method of multipliers (ADMM) to efficiently solve the problem. The conducted experiments on both synthetic and real-world datasets illustrate the effectiveness of the proposed method.

Traffic prediction has become an important and active research topic in the last decade. Existing solutions mainly focus on exploiting the past and current traffic data, collected from various kinds of sensors, such as loop detectors, GPS devices, etc. In real-world road systems, only a small fraction of the road segments are deployed with sensors. For all the other road segments without sensors or historical traffic data, previous methods may no longer work. In this paper, we propose to use location-based social media, which captures a much larger area of the road systems than deployed sensors, to predict the traffic conditions. A simple but effective method called CTP is proposed to incorporate location-based social media semantics into the learning process. CTP also exploits complex dependencies among different regions to improve the prediction performances through collective inference. Empirical studies using traffic data and tweets collected in Los Angeles area demonstrate the effectiveness of CTP.

Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF). Conventional MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. Kernel methods are used widely in SVMs to classify linearly non-separable data, as well as in PCA to discover the non-linear embeddings of data. In this paper, we present a novel method to kernelize matrix factorization for collaborative filtering, which is equivalent to performing the low-rank matrix factorization in a possibly much higher dimensional space that is implicitly defined by the kernel function. Inspired by the success of multiple kernel learning (MKL) methods, we also explore the approach of learning multiple kernels from the rating matrix to further improve the accuracy of prediction. Since the right choice of kernel is usually unknown, our proposed multiple kernel matrix factorization method helps to select effective kernel functions from the candidates. Through extensive experiments on real-world datasets, we show that our proposed method captures the nonlinear correlations among data, which results in improved prediction accuracy compared to the state-of-art CF models.