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Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from lack of efficiency and scalability when dealing with large-scale and high-dimensional data. In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pretraining, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and faster running time of DEPICT in realworld clustering tasks, where no labeled data is available for hyper-parameter tuning.

In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with selfupdated attention. Our VideoQA model firstly generates the global context-aware visual and textual features respectively by interacting current inputs with memory contents. After that, it makes the attentional fusion of the multimodal visual and textual representations to infer the correct answer. Multiple cycles of reasoning can be made to iteratively refine attention weights of the multimodal data and improve the final representation of the QA pair. Experimental results demonstrate our approach achieves state-of-theart performance on four VideoQA benchmark datasets.

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very shortterm load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.Index Terms-Advanced metering infrastructure (AMI), k-means clustering, load forecasting, load patterns, load profiles, neural network-based load forecasting, smart meters.

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Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.

Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications. Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.

For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.