Learning Convolutional Ranking-Score Function by Query Preference Regularization

Zhang, Guohui; Liang, Guoxi; Li, Weizhi; Fang, Jian; Wang, Jingbin; Geng, Yanyan; Wang, Jingyan

doi:10.1007/978-3-319-68935-7_1

Cited by 39 publications

(13 citation statements)

References 28 publications

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“…DFS will integrate information that exists in different servers, so users and administrators can visit resources through resource sharing catalog DFS. Based on this principle, Hadoop has set up HDFS (Hadoop Distributed File System), provides services for the cloud platform as a storage core, and makes storage computation for all files in the cluster, which makes it one of the most widely used DFS at present [14,15].…”

Section: Hadoop Cloud Computationmentioning

confidence: 99%

Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

Liu

Wang

et al. 2018

Complexity

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Reuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big data, the Hadoop platform was used to collect and save data about the public’s cognition towards recycled water in one city and the BP neural network algorithm was used to construct an evaluation model that could affect the public’s cognition level. The public’s risk perception, subjective norm, and perceived behavioral control regarding recycled water reuse were selected as key factors. Based on a multivariate clustering algorithm, MATLAB software was used to make real testing on massive effective data and assumption models, so as to analyze the proportion of three evaluation factors and understand the simulation parameter scope of the cognition degree of different groups of citizens. Lastly, several suggestions were proposed to improve the public’s cognition on recycled water reuse based on the big data in terms of policy mechanism.

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Section: Hadoop Cloud Computationmentioning

confidence: 99%

Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

Liu

Wang

et al. 2018

Complexity

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“…The formulation of well-designed loss functions, along with additional regularization terms, have also been explored in many other domains, such as transfer learning [3,23], deep feature embeddings [10,19], and image retrieval [30] as well as to maximize domainspecific performance metrics [8,13,30]. A very comprehensive and recent survey on deep multimodal learning and regularization can be found in [18].…”

Section: Deep Multimodal Regularizationmentioning

confidence: 99%

On the role of multimodal learning in the recognition of sign language

Ferreira

Cardoso

Rebelo

2018

Multimed Tools Appl

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Sign Language Recognition (SLR) has become one of the most important research areas in the field of human computer interaction. SLR systems are meant to automatically translate sign language into text or speech, in order to reduce the communicational gap between deaf and hearing people. The aim of this paper is to exploit multimodal learning techniques for an accurate SLR, making use of data provided by Kinect and Leap Motion. In this regard, single-modality approaches as well as different multimodal methods, mainly based on convolutional neural networks, are proposed. Our main contribution is a novel multimodal end-to-end neural network that explicitly models private feature representations that are specific to each modality and shared feature representations that are similar between modalities. By imposing such regularization in the learning process, the underlying idea is to increase the discriminative ability of the learned features and, hence, improve the generalization capability of the model. Experimental results demonstrate that multimodal learning yields an overall improvement in the sign recognition performance. In particular, the novel neural network architecture outperforms the current state-of-the-art methods for the SLR task.

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“…Background. Deep learning has been proven to be a powerful tool for pattern classification problems and sensor studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A deep learning model usually has more than three layers, and by using multiple layers, the model extracts hierarchical features from the original data.…”

Section: Introductionmentioning

confidence: 99%

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

Liu

Yan

et al. 2020

Computational Intelligence and Neuroscience

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Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.

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Learning Convolutional Ranking-Score Function by Query Preference Regularization

Cited by 39 publications

References 28 publications

Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

On the role of multimodal learning in the recognition of sign language

Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both Input Data Points and Their Neighbors

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