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Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels [article]

Harris Chan, Jamie Kiros, William Chan
2020 arXiv   pre-print
MGLM marginalizes over all possible factorizations within and across all channels.  ...  In this work, we present the Multichannel Generative Language Model (MGLM). MGLM is a generative joint distribution model over channels.  ...  Acknowledgement We give thanks to Mohammad Norouzi, Lala Li, Sara Sabour, Geoffrey Hinton, Silviu Pitis, and the Google Brain team for useful discussions and feedbacks.  ... 
arXiv:2010.04438v1 fatcat:53jndthyq5ev3itqvhaw6456qy

Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels

Harris Chan, Jamie Kiros, William Chan
2020 Findings of the Association for Computational Linguistics: EMNLP 2020   unpublished
MGLM marginalizes over all possible factorizations within and across all channels.  ...  In this work, we present the Multichannel Generative Language Model (MGLM). MGLM is a generative joint distribution model over channels.  ...  Acknowledgement We give thanks to Mohammad Norouzi, Lala Li, Sara Sabour, Geoffrey Hinton, Silviu Pitis, and the Google Brain team for useful discussions and feedbacks.  ... 
doi:10.18653/v1/2020.findings-emnlp.376 fatcat:jxf6w5oxmrfilcfrrj3z4vt3ia

A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System

Jay Kumar Pandey, Sumit Kumar, Madonna Lamin, Suneet Gupta, Rajesh Kumar Dubey, F. Sammy, Vijay Kumar
2022 Mathematical Problems in Engineering  
Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an effective intrusion detection model.  ...  Next, a one-dimensional convolution neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic.  ...  the feature combination information across channels.  ... 
doi:10.1155/2022/3859155 fatcat:ku6u6x7vnramjbzu2v5asjysg4

Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks [article]

Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang, Tie-Yan Liu
2018 arXiv   pre-print
To verify the effectiveness of MC-RNN, we conduct extensive experiments on typical natural language processing tasks, including neural machine translation, abstractive summarization, and language modeling  ...  Recurrent Neural Networks (RNNs) have been widely used in processing natural language tasks and achieve huge success. Traditional RNNs usually treat each token in a sentence uniformly and equally.  ...  the Central Universities and SAFEA: Overseas Young Talents in Cultural and Educational Sector.  ... 
arXiv:1811.05121v1 fatcat:zdxqssw36rejrbc6zlpm5wwt2a

Channels in the Mirror: An Alignable Model for Assessing Customer Satisfaction in Concurrent Channel Systems

Maik Hammerschmidt, Tomas Falk, Bert Weijters
2019 Figshare  
The resulting 5C model is superior to existing models in that it enables the unified capture of both off-line and online satisfaction, allowing a meaningful comparison across formats.  ...  The 5C model also supports within-channel decisions by revealing the impact of the five facets on overall satisfaction with each format.  ...  Acknowledgment The authors thank Editor Mary Jo Bitner, former Editor Kay Lemon, and three anonymous reviewers as well as Deva Rangarajan and Niels Schillewaert for helpful comments and suggestions for  ... 
doi:10.6084/m9.figshare.7622069 fatcat:qgreujnq4ndb5fkfbxemfhx4va

Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks

Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang, Tie-Yan Liu
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To verify the effectiveness of MC-RNN, we conduct extensive experiments on typical natural language processing tasks, including neural machine translation, abstractive summarization, and language modeling  ...  Recurrent Neural Networks (RNNs) have been widely used in processing natural language tasks and achieve huge success. Traditional RNNs usually treat each token in a sentence uniformly and equally.  ...  the Central Universities and SAFEA: Overseas Young Talents in Cultural and Educational Sector.  ... 
doi:10.1609/aaai.v33i01.33015525 fatcat:qvfsongkmvdv7otx3n5rgquevu

Interactive dialogue model: a design technique for multichannel applications

D. Bolchini, P. Paolini
2006 IEEE transactions on multimedia  
of consistency across the different channels and the perception that they are "different applications").  ...  This paper presents an interactive dialogue model (IDM), a novel design model specifically tailored for multichannel applications.  ...  Different factors are being implied here. 1) It must be easy to teach the design methodology (and the design model) to anyone (from students to practitioners invest for learning new methodologies; one  ... 
doi:10.1109/tmm.2006.870733 fatcat:qk36zysvwzfbvh6egjytxkngze

Raw Multichannel Processing Using Deep Neural Networks [chapter]

Tara N. Sainath, Ron J. Weiss, Kevin W. Wilson, Arun Narayanan, Michiel Bacchiani, Bo Li, Ehsan Variani, Izhak Shafran, Andrew Senior, Kean Chin, Ananya Misra, Chanwoo Kim
2017 New Era for Robust Speech Recognition  
compared to a single channel waveform model.  ...  Multichannel ASR systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling.  ...  Even though the time and frequency models all learn different spatial filters, they all seem to have similar WERs.  ... 
doi:10.1007/978-3-319-64680-0_5 fatcat:22k7btluzvalnf7sbepu5w5pae

Extension of hidden markov model for recognizing large vocabulary of sign language [article]

Maher Jebali, Patrice Dalle, Mohamed Jemni
2013 arXiv   pre-print
Unlike speech recognition, Frensh sign language (FSL) events occur both sequentially and simultaneously. Thus, the computational processing of FSL is too complex than the spoken languages.  ...  In this work, we are dealing with sign language recognition, in particular of French Sign Language (FSL).  ...  A multi-dimensional HMM is capable to deal with multichannel signs which are general cases of sign language recognition.  ... 
arXiv:1304.3265v1 fatcat:wp32trwimrfx5b2a65okkch47a

Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models

Alex Zarifis, Christopher P. Holland, Alistair Milne
2019 Emerald Open Research  
In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers.  ...  In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness.  ...  The constructs were used for within case analysis and across case analysis.  ... 
doi:10.35241/emeraldopenres.13249.1 fatcat:ododxeelsbfsfammzy4rdglcim

MIC: Model-agnostic Integrated Cross-channel Recommenders [article]

Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren
2022 arXiv   pre-print
Specifically, MIC robustly models correlation within user-item, user-user, and item-item from latent interactions in a universal schema.  ...  In this paper, we propose a model-agnostic integrated cross-channel (MIC) approach for the large-scale recommendation, which maximally leverages the inherent multi-channel mutual information to enhance  ...  Specifically, MIC models correlation across user-item (U2I), user-user (U2U), and item-item (I2I) channels via intra and inter cross-channel contrastive modules.  ... 
arXiv:2110.11570v2 fatcat:i52iepprnzdxtkc5sj4ja23jcy

Recent Progresses in Deep Learning based Acoustic Models (Updated) [article]

Dong Yu, Jinyu Li
2018 arXiv   pre-print
We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.  ...  In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.  ...  It is obvious that each output pixel is a weighted sum of all pixels across all channels in an input patch.  ... 
arXiv:1804.09298v2 fatcat:yfxzxu6qanbndcnmt3loikqeym

Recent progresses in deep learning based acoustic models

Dong Yu, Jinyu Li
2017 IEEE/CAA Journal of Automatica Sinica  
We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.  ...  In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.  ...  It is obvious that each output pixel is a weighted sum of all pixels across all channels in an input patch.  ... 
doi:10.1109/jas.2017.7510508 fatcat:zcffvbg75bhllcekqghkmwidsy

Extension of Hidden Markov Model for Recognizing Large Vocabulary of Sign Language

Maher Jebali, Patrice Dalle, Mohamed Jemni
2013 International Journal of Artificial Intelligence & Applications  
Unlike speech recognition, Frensh sign language (FSL) events occur both sequentially and simultaneously. Thus, the computational processing of FSL is too complex than the spoken languages.  ...  In this work, we are dealing with sign language recognition, in particular of French Sign Language (FSL).  ...  A multi-dimensional HMM is capable to deal with multichannel signs which are general cases of sign language recognition.  ... 
doi:10.5121/ijaia.2013.4203 fatcat:fe3foqvrdvazld375bixarzlbe

A Deep Neural Model Of Emotion Appraisal [article]

Pablo Barros, Emilia Barakova, Stefan Wermter
2018 arXiv   pre-print
We deployed the model in an iCub robot and evaluated the capability of the robot to learn and describe the affective behavior of different persons based on observation.  ...  In this paper, we propose a deep neural model which is designed in the light of different aspects of developmental learning of emotional concepts to provide an integrated solution for internal and external  ...  The modulation factor is defined as γ and it has a value within the interval of [0, 1].  ... 
arXiv:1808.00252v1 fatcat:zp3qgbqafrdofaxbeykss6g7oy
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