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Hybrid Feature Embedded Sparse Stacked Autoencoder and Manifold Dimensionality Reduction Ensemble for Mental Health Speech Recognition
2021
IEEE Access
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ACKNOWLEDGMENT The authors would like to thank the editor and reviewers for their valuable comments and suggestions. ...
We also would like to thank those individuals (or institutions) that have provided data support for this research. ...
algorithm of mental health based on embedded hybrid feature stacked sparse autoencoder ensemble. ...
doi:10.1109/access.2021.3057382
fatcat:eswqldx2qfhppkcy3maxemvfru
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
[article]
2021
arXiv
pre-print
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distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). ...
There are two main drawbacks to this approach: firstly, the feature engineering being manual is cumbersome and requires human knowledge; and secondly, the designed features might not be best for the objective ...
Manifold learning aims to describe data as low-dimensional manifolds embedded in high-dimensional spaces [93] . ...
arXiv:2001.00378v2
fatcat:ysvljxylwnajrbowd3kfc7l6ve
A Review of Deep Learning Algorithms and Their Applications in Healthcare
2022
Algorithms
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autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language ...
Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the ...
Acknowledgments: The authors would like to thank the Arab Open University for supporting this research paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/a15020071
fatcat:ku5mfuijdjfxxdv7hlkexad7dy
Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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Knowledge and Voicing Features Based on DNN/HMM for Mandarin Speech Recognition [#15184] Tan Ying-Wei, Liu Wen-Ju, Jiang Wei and Zheng Hao P178 Evaluation of Optical Flow Field Features for the Detection ...
Session Manif2: Manifold learning and dimensionality reduction 2
Monday, July 13, 1:30PM-2:30PM, Room: Brehon, Chair: Wang, Xiaoping
1:30PM Improved Manifold Learning with Competitive Hebbian Rule ...
doi:10.1109/ijcnn.2015.7280446
dblp:conf/ijcnn/YangLNF15
fatcat:6xlakikcfzfyhhm2spooe2j7ra
Use of Transfer Learning for Automatic Dietary Monitoring through Throat Microphone Recordings
2019
Zenodo
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The teacher network is trained over abundant high-quality CM recordings, whereas the student network takes TM recordings as input and distills deep feature extraction capacity of the teacher over a parallel ...
This system uses the TM sensor as a non-invasive transducer mounted on the neck, which is capable of delivering robust signal recordings for intelligent and unobtrusive food intake monitoring. ...
A dimensionality reduction step is then performed through a generalized eigenvalue decomposition process to eliminate feature redundancy. ...
doi:10.5281/zenodo.3841956
fatcat:ncalroecszg3hhpc45havcxhee
Use of Transfer Learning for Automatic Dietary Monitoring through Throat Microphone Recordings
2019
Zenodo
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The teacher network is trained over abundant high-quality CM recordings, whereas the student network takes TM recordings as input and distills deep feature extraction capacity of the teacher over a parallel ...
This system uses the TM sensor as a non-invasive transducer mounted on the neck, which is capable of delivering robust signal recordings for intelligent and unobtrusive food intake monitoring. ...
A dimensionality reduction step is then performed through a generalized eigenvalue decomposition process to eliminate feature redundancy. ...
doi:10.5281/zenodo.3841957
fatcat:so4kiaj4ljbw5aay36xd6dlx2q
A Survey of Deep Active Learning
[article]
2021
arXiv
pre-print
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Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. ...
Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL. ...
data points
overly representing sample points from manifold sparse areas. ...
arXiv:2009.00236v2
fatcat:zuk2doushzhlfaufcyhoktxj7e
Interpretable machine learning: Fundamental principles and 10 grand challenges
2022
Statistics Survey
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unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints ...
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. ...
Acknowledgments We thank Leonardo Lucio Custode for pointing out several useful references to Challenge 10. Thank you to David Page for providing useful references on early explainable ML. ...
doi:10.1214/21-ss133
fatcat:ahzfoilhmfa2rd4hcauvsn3eyy
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
[article]
2021
arXiv
pre-print
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unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints ...
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. ...
Acknowledgments We thank Leonardo Lucio Custode for pointing out several useful references to Challenge 10. Thank you to David Page for providing useful references on early explainable ML. ...
arXiv:2103.11251v2
fatcat:52llnswt3ze5rl3zhbai5bscce
A Roadmap for Big Model
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.14101v2 fatcat:zjl2fte3njc43cxbtjhcp5ypwuOther Versions
In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies ...
and Application. ...
[1017] , and speech recognition [1018] . ...
arXiv:2203.14101v4
fatcat:rdikzudoezak5b36cf6hhne5u4
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One
2018
BMC Neuroscience
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Trensch and R. Gutzen for fruitful discussions. ...
Acknowledgements This work was inspired by the debate"Direct and indirect pathways:
Acknowledgements We thank Ramon Huerta for his helpful discussions and Javier Perez-Orive and Gilles Laurent for providing ...
Are sparse representations useful for neuronal pattern recognition, and under what conditions? ...
doi:10.1186/s12868-018-0452-x
pmid:30373544
pmcid:PMC6205781
fatcat:xv7pgbp76zbdfksl545xof2vzy
30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts
2021
Journal of Computational Neuroscience
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Currently, most functional models of neural activity are based on firing rates, while the most relevant signals for inter-neuron communication are spikes. ...
As sparse coding is beneficial for pattern recognition, this result predicts that clustered MF input would improve the storage capacity of the cerebellar cortex. ...
of Health. ...
doi:10.1007/s10827-021-00801-9
pmid:34931275
pmcid:PMC8687879
fatcat:evpmmfpaivgpxdqpive5xdgmwu
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
[chapter]
2022
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
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Chronic Diseases are the most dangerous diseases for humans and have significant effects on human life. Chronic Diseases like heart disease & Diabetes are the main causes of death. ...
Precise diagnosis of these diseases on time is very significant for maintaining a healthy life. ...
This work implements k-mean segmentation for segmenting the pre-processed image. The region-based segmentation will segment the data dependent on the taken-out features using GLCM algorithm. ...
doi:10.13052/rp-9788770227667
fatcat:da47mjbbyzfwnbpde7rgbrlppe
29th Annual Computational Neuroscience Meeting: CNS*2020
2020
BMC Neuroscience
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Hence, feedback from downstream visual cortical areas to V1 for better decoding (recognition), through analysis-by-synthesis, should query for additional information and be mainly directed at the foveal ...
Deep RL offers a rich framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses ...
Acknowledgements: This research is funded by the National Science Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California Nano-Systems ...
doi:10.1186/s12868-020-00593-1
pmid:33342424
fatcat:edosycf35zfifm552a2aogis7a
EMG-based Simultaneous and Proportional Estimation of Wrist Kinematics and its Application in Intuitive Myoelectric Control for Unilateral transradial Amputees
2011
Frontiers in Computational Neuroscience
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Acknowledgements This work was funded by the BFNT-B3 and by the SFB780
[T 62] Model-invariant features of correlations in recurrent networks Moritz Helias 1* , Dmytro Grytskyy 1 , Tom Tetzlaff 1 and ...
(DFG) and the Intramural Research Program (IRP) of the National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Health (NIH). ...
Analyses revealed that cognitively relevant network states were embedded in a lower-dimensional nonlinear manifold within the high-dimensional space. ...
doi:10.3389/conf.fncom.2011.53.00081
fatcat:nkq5wesfpbcjnikbxqh3v3gtqi
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