[PDF] Top 20 Deep Neural Networks for Recommender Systems

Deep Neural Networks for Recommender Systems

... it. Recommender Systems are effective software techniques to overcome this ...interest. Recommender systems have wide applications like providing suggestive list of items to customers for ... See full document

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Recurrent Neural Networks for Recommender Systems

... Based Recommender Systems A unique characteristic of recommender systems is considered the extent of how Recurrent Neural Networks can be applied to ...of deep learning ... See full document

6

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

... Recurrent Neural Network Model We adopting a generative view on sequences and in the same spirit as latent factor models (Koren et ...Recurrent neural networks are powerful sequence models that ... See full document

16

Trust-Networks in Recommender Systems

... the recommender system by calculating the total coverage of the ...a recommender system can provide predictions [1]. A recommender system may not be able to make predictions on every ...a ... See full document

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Trust networks for recommender systems

... by issuing a few trust statements, compared to a similar amount of rating infor- mation, the system can generate more, and more accurate, recommendations [91] (more on this topic in Chapter 7). Moreover, a web of trust ... See full document

236

Explainable Neural Attention Recommender Systems

... Latent-based collaborative filtering, therefore, is a recommendation paradigm that attempts to represent users and items in a way that captures their preferences and attributes, respectively, in a way that is both ... See full document

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TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding

... SNNs, deep neural networks (DNNs) have been able to perform the state-of-the-art results on many complex tasks such as image recognition (Krizhevsky, Sutskever, and Hinton 2012; Krizhevsky 2009; ... See full document

8

On explainability of deep neural networks

... Adversarial Networks (GANs), proposed by Goodfellow in [26], that have achieved state of the art performance in realistic image generation, among many other ...preserving networks, that also encode the ... See full document

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Distributed deep neural networks

... Figure 15: TensorFlow hardware and software stack [30] The Delay Compensated SGD and Downpour SGD implemented for report were implemented in Python, results might have differed if this experiment was replicated at the C ... See full document

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Deep morphological neural networks

... of deep morphological neural ...residual neural network for shape classification. Morphological residual neural network achieves a great tradeoff between model accuracy and number of ... See full document

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On Recurrent and Deep Neural Networks

... The content of this thesis overlaps with seven di↵erent papers that I published while doing my studies, and, some of the content of the thesis has been borrowed directly from these works. As most research carried out in ... See full document

267

Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks

... “sharpen” them into Spiking Deep Neural Networks (SDNNs). These are similar to SNNs, but contain some enhanced functionality beyond SNNs. The original goal of this project was to leverage the ... See full document

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Using Graph Neural Networks to model the performance of Deep Neural Networks

... for neural networks, such as Halide or TVM, incorporate a machine learning-based performance model to search the space of valid implementations of a given deep learning ...feed-forward ... See full document

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Orthogonal Recurrent Neural Networks and Batch Normalization in Deep Neural Networks

... Recurrent Neural Networks and Batch Normalization in Deep Neural Networks Despite the recent success of various machine learning techniques, there are still numerous obstacles that must ... See full document

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Deep Learning based Trust Aware Recommender for Social Networks

... B. Proposed System Overview This paper mainly focuses on the Trust-Based recommendations; Memory-based approaches have largely figured on integrating trust into recommendations. The most common RSs cause users to issue ... See full document

7

Polymorphic Accelerators for Deep Neural Networks

... Abstract—Deep neural networks (DNNs) come with many forms, such as convolutional neural networks, multilayer perceptron and recurrent neural networks, to meet diverse ... See full document

14

Modeling Interestingness with Deep Neural Networks

... a deep semantic simi- larity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target docu- ments to be of interest to a user based on a source ... See full document

12

Completeness Problem of the Deep Neural Networks

... After Hinton’s initial attempt of training one layer at a time, Deep Neural Networks train all layers together. Examples include TensorFlow [6], Torch [7], and Theano [8]. Google’s TensorFlow is an ... See full document

13

Sensitivity Analysis of Deep Neural Networks

... Deep neural networks (DNNs) have exhibited impressive power in image classification and outperformed human de- tection in the ImageNet challenge (Russakovsky et ... See full document

8

Genomic Selection with Deep Neural Networks

... applied neural networks to recognize handwritten text or generate transcripts of spoken words from real-time audio recordings ( Lang et ...a neural networks model to a traditional lo- gistic ... See full document

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