Deep Learning is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses on the development and application of neural networks, which are computational models inspired by the structure and function of the human brain. Deep learning algorithms aim to learn and represent data in increasingly abstract and hierarchical ways, allowing them to automatically discover patterns, features, and representations from raw input data.

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TGNet: Learning to Rank Nodes in Temporal Graphs

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Node ranking in temporal networks are often impacted by heterogeneous context from node content, temporal, and structural dimensions. This paper introduces TGNet , a deep-learning framework for node ranking in heterogeneous temporal graphs. TGNet utilizes a variant of Recurrent Neural Network to adapt context evolution and extract context features for nodes. It incorporates a novel influence network to dynamically estimate temporal and structural influence among nodes over time. To cope with label sparsity, it integrates graph smoothness constraints as a weak form of supervision. We show that the application of TGNet is feasible for large-scale networks by developing efficient learning and inference algorithms with optimization techniques. Using real-life data, we experimentally verify the effectiveness and efficiency of TGNet techniques. We also show that TGNet yields intuitive explanations for applications such as alert detection and academic impact ranking, as verified by our case study.

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Learning Gibbs-Regularized Pushforward Density Estimators with a Symmetric KL Objective

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We claim that there is currently no satisfactory way to regularize a generative adversarial network (GAN): neither the generator nor discriminator is particularly amenable to the imposition of inductive biases derived from domain knowledge. A generator is effectively a causal model of generation—one that usually bears no resemblance to the true generation process, which is most often unobserved or exceedingly difficult to model. Consider image generation: although it is plausible—e.g., from biological arguments—that convolutional neural networks constitute a good class of image classifiers, claiming CNNs are inherently well-suited to image generation is harder to justify. Likewise, it is clear that regularizing the discriminator is necessary to prevent trivial solutions; although recent methods have seen some success in applying generic smoothness regularizers to the discriminator [1, 5, 12], it is not obvious how to impose domain-specific structure on the discriminator in an optimal way

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Deep Learning IP Network Representations

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We present DIP, a deep learning-based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts the distance between them more than 30% better than a mean-based approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security.

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Deep r-th Root Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval

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Multivariate time series data are becoming increasingly common in numerous real-world applications, e.g., power plant monitoring, health care, wearable devices, automobiles, etc. As a result, multivariate time series retrieval, i.e., given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data (or in the database), attracts a significant amount of interest in many fields. Building such a system, however, is challenging since it requires a compact representation of the raw time series, which can explicitly encode the temporal dynamics as well as the correlations (interactions) between different pairs of time series (sensors). Furthermore, it requires query efficiency and expects a returned ranking list with high precision on the top. Despite the fact that various approaches have been developed, few of them can jointly resolve these two challenges. To cope with this issue, in this paper, we propose a Deep r-th root of Rank Supervised Joint Binary Embedding (Deep r-RSJBE) to perform multivariate time series retrieval. Given a raw multivariate time series segment, we employ Long Short-Term Memory (LSTM) units to encode the temporal dynamics and utilize Convolutional Neural Networks (CNNs) to encode the correlations (interactions) between different pairs of time series (sensors). Subsequently, a joint binary embedding is pursued to incorporate both the temporal dynamics and the correlations. Finally, we develop a novel r-th root ranking loss to optimize the precision at the top of a Hamming distance ranking list. Thoroughly empirical studies based upon three publicly available time series datasets demonstrate the effectiveness and the efficiency of Deep r-RSJBE.

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