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CoNet: Collaborative Cross Networks for Cross-Domain Recommendation [article]

Guangneng Hu, Yu Zhang, Qiang Yang
2018 arXiv   pre-print
We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet).  ...  In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model.  ...  Conclusions We proposed a novel approach to perform knowledge transfer learning for cross-domain recommendation via collaborative cross networks (CoNet).  ... 
arXiv:1804.06769v2 fatcat:g5t3u3vxjbahbj7gpx2mwteh54

Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework

Zhong Yin, Mengyuan Zhao, Wei Zhang, Yongxiong Wang, Yagang Wang, Jianhua Zhang
2019 Neurocomputing  
We also investigated how to select TDAE hyper-parameters and found its superiority in accuracy can be achieved with proper filter orders.  ...  The TDAE consists of three consecutively-connected modules, which are termed as feature filter, abstraction filter, and transferred MW classifier.  ...  Transfer learning emphasizes sharing knowledge from a source domain to a target domain [17] .  ... 
doi:10.1016/j.neucom.2019.02.061 fatcat:cdrrds5du5ah5h57d2baha6hdi

Graph Factorization Machines for Cross-Domain Recommendation [article]

Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He
2020 arXiv   pre-print
However, most existing cross-domain recommendation methods might fail when confronting the graph-structured data.  ...  Besides, based on general cross-domain recommendation experiments, we also demonstrate that our cross-domain framework could not only contribute to the cross-domain recommendation task with the GFM, but  ...  However, it is hard for the traditional collaborative filtering methods to utilize the graph-structured data.  ... 
arXiv:2007.05911v1 fatcat:f6xugvw5ifglzeprw542gad72u

Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation [article]

Weiming Liu, Xiaolin Zheng, Mengling Hu, Chaochao Chen
2022 arXiv   pre-print
To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem.  ...  Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems.  ...  for Cross-Domain Recommendation via Transferring Rating Patterns (DARec) adopts adversarial training strategy to extract and transfer knowledge patterns for shared users across domains.  ... 
arXiv:2202.04920v1 fatcat:lqnltllc4ngvjmp47iqww3kg5i

Model recommendation: Generating object detectors from few samples

Yu-Xiong Wang, Martial Hebert
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Army Research Laboratory (ARL) under the Collaborative Technology Alliance Program, Cooperative Agreement W911NF-10-2-0016, and by an AWS in Education Coursework Grant.  ...  However, models informative across categories and datasets could be achieved via unsupervised hyper-training.  ...  Collaborative Filtering Based on the probe set ratings and the ratings store, collaborative filtering techniques predict the ratings of the entire library.  ... 
doi:10.1109/cvpr.2015.7298770 dblp:conf/cvpr/WangH15 fatcat:auejhxzbibb7zb5o3fmjfay2de

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 8043-8054 Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning.  ...  Meng, M., +, TIP 2020 186-198 Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination

Zhong Yin, Yongxiong Wang, Li Liu, Wei Zhang, Jianhua Zhang
2017 Frontiers in Neurorobotics  
The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database.  ...  A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting.  ...  The linear model has a parsimonious structure than the non-linear model and the SVM follows the principle of the structural risk minimization.  ... 
doi:10.3389/fnbot.2017.00019 pmid:28443015 pmcid:PMC5385370 fatcat:p274i35vpfbrlga4pfszdhs5qy

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation [article]

Xu Chen and Ya Zhang and Ivor Tsang and Yuangang Pan and Jingchao Su
2022 arXiv   pre-print
Cross domain recommendation (CDR) is one popular research topic in recommender systems.  ...  The majority of recent methods have explored the shared-user representation to transfer knowledge across domains.  ...  CCCFNet [51] combines collaborative filtering and content-based filtering into one unified matrix factorization framework.  ... 
arXiv:2009.06884v2 fatcat:aeyzv4kotbakrirf4mdhqt6luy

Learning a meta-level prior for feature relevance from multiple related tasks

Su-In Lee, Vassil Chatalbashev, David Vickrey, Daphne Koller
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
We show that transfer learning of feature relevance improves performance on two real data sets which illustrate such settings: (1) predicting ratings in a collaborative filtering task, and (2) distinguishing  ...  Our approach transfers the meta-priors among different tasks, allowing it to deal with settings where tasks have non-overlapping features or where feature relevance varies over the tasks.  ...  Traditionally, a prior for feature relevance is selected by hand, or via cross-validation.  ... 
doi:10.1145/1273496.1273558 dblp:conf/icml/LeeCVK07 fatcat:peh5k4pmdrhsrdmqd7s54cfgdu

A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation

Wenxing Hong, Nannan Zheng, Ziang Xiong, Zhiqiang Hu
2020 IEEE Access  
INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction.  ...  In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation.  ...  Cross-domain recommendation is a technology for knowledge transfer between multiple domains.  ... 
doi:10.1109/access.2020.2977123 fatcat:4lfqtfpt4jdplf2j66do2p325y

Bilinear noise subtraction at the GEO 600 observatory [article]

Nikhil Mukund, James Lough, Christoph Affeldt, Fabio Bergamin, Aparna Bisht, Marc Brinkmann, Volker Kringel, Harald Lück, Séverin Landry Nadji, Michael Weinert
2020 arXiv   pre-print
The time-domain filtering efficiency is observed to depend upon the system identification process especially when the involved transfer functions cover a large dynamic range and have multiple resonant  ...  The filter coefficients are updated periodically to account for any non-stationarities that can arise within the coupling.  ...  Updating the filter coefficients to tackle the non-stationaries has recently been shown to provide better subtraction for the case of non-linear noise observed in the LIGO detectors [38] .  ... 
arXiv:2001.00242v1 fatcat:tcfeaoupubglfk7abumzhczrvu

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [article]

Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu
2021 arXiv   pre-print
We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used.  ...  In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks.  ...  They proposed a new knowledge transfer technique, called the hyper-structure transfer (HST) [61] , that captured the non-linear correlations of knowledge between domains.  ... 
arXiv:2108.03357v1 fatcat:sitcklnxibafjomlq77rqvboia

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Sun, Y., +, TIP 2021 6277-6291 Handheld computers Spatial-Spectral Structured Sparse Low-Rank Representation for Hyper-TIP 2021 1332-1341 DotFAN: A Domain-Transferred Face Augmentation Net.  ...  Imaging via Non-Iterative Subspace-Based Fusion.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification

Hao Wu, Yi Niu, Fu Li, Yuchen Li, Boxun Fu, Guangming Shi, Minghao Dong
2019 Frontiers in Neuroscience  
In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification.  ...  We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features.  ...  Additionally, square and log non-linear operations enhance the non-linear expression ability of the feature reduction layer.  ... 
doi:10.3389/fnins.2019.01275 pmid:31849587 pmcid:PMC6901997 fatcat:yot5qoufcbgh3mahxiswvvccc4

Deep Learning and Domain Transfer for Orca Vocalization Detection

Paul Best, Maxence Ferrari, Marion Poupard, Sebastien Paris, Ricard Marxer, Helena Symonds, Paul Spong, Herve Glotin
2020 2020 International Joint Conference on Neural Networks (IJCNN)  
In this paper, we study the difficulties of domain transfer when training deep learning models, on a specific task that is orca vocalization detection.  ...  We thus explore approaches to compensate on the difficulties faced with domain transfer, with two convolutionnal neural networks (CNN) architectures, one that works in the time-frequency domain, and one  ...  We thank first the OrcaLab direction Paul Spong and Helena Symonds and collaborators for their incredible inspired work.  ... 
doi:10.1109/ijcnn48605.2020.9207567 dblp:conf/ijcnn/BestFPPMSSG20 fatcat:m5bk5pdeczhnbb7niu6toojqd4
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