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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farkaš,Paolo Masulli,Stefan Wermter Conference proc

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楼主: FERAL
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Advances in Password Recovery Using Generative Deep Learning Techniquesistic password candidates. In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: ., . and .. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art sampling performance
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Generating Math Word Problems from Equations with Topic Consistency Maintaining and Commonsense Enfo generation task – generating math word problems from equations and propose a novel equation-to-problem text generation model. Our model first utilizes a template-aware equation encoder and a Variational AutoEncoder (VAE) model to bridge the gap between abstract math tokens and text. We then introdu
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Joint Graph Contextualized Network for Sequential Recommendationre transitions of items by treating session sequences as graph-structured data. However, existing graph construction approaches mainly focus on the directional dependency of items and ignore benefits of feature aggregation from undirectional relationship. In this paper, we innovatively propose a joi
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LGACN: A Light Graph Adaptive Convolution Network for Collaborative Filteringnvolutional Network (GCN) has become a new frontier technology of collaborative filtering. However, existing methods usually assume that neighbor nodes have only positive effects on the target node. A few methods analyze the design of traditional GCNs and eliminate some invalid operations. However,
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HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphsout their nefarious tasks. To address this issue, analysts have developed systems that can prevent malware from successfully infecting a machine. Unfortunately, these systems come with two significant limitations. First, they frequently target one specific platform/architecture, and thus, they canno
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