A survey on deep learning and its applications

Citation edge evidence

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• A survey on deep learning and its applications→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• A survey on deep learning and its applications→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-…Legacy edge — source detail unavailable
• A survey on deep learning and its applications→Advances in Neural Information Processing Systems 28Legacy edge — source detail unavailable
• A survey on deep learning and its applications→Advances in neural information processing systems 7Legacy edge — source detail unavailable
• A survey on deep learning and its applications→Advances in Neural Information Processing Systems 29Legacy edge — source detail unavailable
• A survey on deep learning and its applications→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Learning Deep Architectures for AILegacy edge — source detail unavailable
• A survey on deep learning and its applications→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Learning Deep Architectures for AILegacy edge — source detail unavailable
• A survey on deep learning and its applications→Graph Convolutional Neural Networks for Web-Scale Recommender SystemsLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Conditional Random Fields as Recurrent Neural NetworksLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Multi-Scale Context Aggregation by Dilated ConvolutionsLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Deep Learning on Graphs: A SurveyLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Support Vector MachinesLegacy edge — source detail unavailable
• A survey on deep learning and its applications→Graph Neural Networks: A Review of Methods and ApplicationsLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Advances in kernel methods: support vector learningLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Dropout: a simple way to prevent neural networks from overfitting→Reducing the Dimensionality of Data with Neural NetworksLegacy edge — source detail unavailable
• Dropout: a simple way to prevent neural networks from overfitting→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Reducing the Dimensionality of Data with Neural Networks→A Fast Learning Algorithm for Deep Belief NetsLegacy edge — source detail unavailable
• Semi-Supervised Classification with Graph Convolutional Networks→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Graph Neural Networks: A Review of Methods and Applications→Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-…Legacy edge — source detail unavailable
• Graph Neural Networks: A Review of Methods and Applications→Deep Learning on Graphs: A SurveyLegacy edge — source detail unavailable
• Deep Learning on Graphs: A Survey→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• Deep Learning on Graphs: A Survey→Graph Convolutional Neural Networks for Web-Scale Recommender SystemsLegacy edge — source detail unavailable