Abstract: DCD is a popular optimization method. Sequential DCD makes parallelization difficult. Due to concurrent coordinate updates, running multiple DCD threads on batches of data elements causes inaccuracy and slow convergence. Some parallelization methods use…
Abstract: Attention helps model sequences and learn a deep representation. Recent studies show that attention values do not always match intuition in tasks like machine translation and sentiment classification. This study uses deep reinforcement learning…
Abstract: Multiview subspace clustering (MVSC) is a new method that finds the subspace in multiview data and clusters it. Many MVSC methods have been proposed recently, but most of them cannot explicitly preserve locality in…
Abstract: Unsupervised cross-domain fault diagnosis has been studied recently. It learns transferable features that reduce distribution inconsistency between source and target domains without target supervision. Most cross-domain fault diagnosis methods assume source and target fault…
Abstract: Deep image prior (DIP) uses a deep convolutional network (ConvNet) structure as an image prior. DIP empirically proves ConvNet structures work for image restoration. Why the DIP works well is unknown. Convolution’s benefit in…
Abstract: Unbalanced data can strongly bias classifiers toward the majority class. GAN methods have been proposed to address this issue. Existing GAN-based methods fail to exploit the classifier-generator relationship. This article proposes a novel three-player…
Abstract: Recently, AI has garnered attention. Privacy, security, and model fairness issues have arisen alongside its advances. Differential privacy, a promising mathematical model, can help solve these problems. For this reason, differential privacy has been…
Abstract: Because user preferences change over time, capturing them is essential to predicting user behavior. Many shallow and deep recommendation algorithms model user static and dynamic preferences separately, making it difficult to fuse them for…
Abstract: Class imbalance problem that characterized with the skew distribution towards the majority arises as one challenge in recent years. Many oversampling techniques have been proposed to cope with this problem and some of them…
Abstract: Link prediction (LP) in networks predicts future interactions between elements and is a crucial machine-learning tool in genomics, social networks, marketing, and e-commerce recommender systems. Many LP techniques have been developed in the past,…
Abstract: Fuzzy discrete event systems (FDESs) can model event-driven systems as fuzzy automata with ambiguous states and event-invoked state transitions. In two recent papers, we developed algorithms for online-supervised learning of the fuzzy automaton’s event…
Abstract: Many successful methods have been proposed for learning low-dimensional representations on large-scale networks, but almost all are designed in inseparable processes, learning embeddings for entire networks even when only a small percentage of nodes…
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