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Paper Citation Record · LEDGER

Survey on Deep Neural Networks in Speech and Vision Systems

As of 15 August 2026, this Paper Citation Record lists 100 of 233 outbound references and 0 inbound Pith citation observations for arXiv:1908.07656.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.07656 v2

Coverage vector

measured 100 of 233 reference resolution

Typed states for the displayed outbound observations.

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measured 100 of 100 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 233 outbound references displayed

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External citation measurements

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Outbound references

Observation 9802fe7e-e35a-4ddb-82d6-aeb0ff5f0e71 · outbound

This paper cites Driver inattention monitoring system for intelligent vehicles: A review,.

Survey on Deep Neural Networks in Speech and Vision Systems Driver inattention monitoring system for intelligent vehicles: A review,

Reference 1

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This paper cites Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation,.

Survey on Deep Neural Networks in Speech and Vision Systems Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation,

Reference 2

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Observation 335e3c92-4dee-4a41-8f12-a7a801dd97e8 · outbound

This paper cites A Review of Computer Vision Techniques for the Analysis of Urban Traffic,.

Survey on Deep Neural Networks in Speech and Vision Systems A Review of Computer Vision Techniques for the Analysis of Urban Traffic,

Reference 3

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Observation a7500238-0b10-4c1e-b714-b3ed6ad91f0d · outbound

This paper cites Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles,.

Survey on Deep Neural Networks in Speech and Vision Systems Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles,

Reference 4

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Observation 1109e25c-6cf2-4150-9514-a54386c19326 · outbound

This paper cites End to End Learning for Self-Driving Cars.

Survey on Deep Neural Networks in Speech and Vision Systems End to End Learning for Self-Driving Cars

Reference 5

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Observation c986f42e-207e-4716-8a4c-e7c594cc024f · outbound

This paper cites Lane-Change Detection Based on Vehicle-Trajectory Prediction,.

Survey on Deep Neural Networks in Speech and Vision Systems Lane-Change Detection Based on Vehicle-Trajectory Prediction,

Reference 6

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This paper cites Single-pedestrian detection aided by two-pedestrian detection,.

Survey on Deep Neural Networks in Speech and Vision Systems Single-pedestrian detection aided by two-pedestrian detection,

Reference 7

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Observation e5db191b-8100-49e6-96d8-420c1251b616 · outbound

This paper cites Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning,.

Survey on Deep Neural Networks in Speech and Vision Systems Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning,

Reference 8

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Observation f72cae4d-5e06-44db-83d0-33cb29e58bba · outbound

This paper cites Capturing Car-Following Behaviors by Deep Learning,.

Survey on Deep Neural Networks in Speech and Vision Systems Capturing Car-Following Behaviors by Deep Learning,

Reference 9

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Observation 4249d6c4-cb8f-46c4-825b-b478b0e5ddf0 · outbound

This paper cites Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview,.

Survey on Deep Neural Networks in Speech and Vision Systems Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview,

Reference 10

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Observation 59c61f42-26b3-4574-b158-a6043c0eadd5 · outbound

This paper cites Brain tumor segmentation with Deep Neural Networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Brain tumor segmentation with Deep Neural Networks,

Reference 11

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Observation f4c45c3b-762b-4d31-bdfc-500325342473 · outbound

This paper cites Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease,.

Survey on Deep Neural Networks in Speech and Vision Systems Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease,

Reference 12

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Observation 827f7863-dde4-47e3-b0c6-118d0ab69d96 · outbound

This paper cites Deep biomarkers of human aging: Application of deep neural networks to biomarker development,.

Survey on Deep Neural Networks in Speech and Vision Systems Deep biomarkers of human aging: Application of deep neural networks to biomarker development,

Reference 13

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Observation 463c5b2d-66c1-44db-bf94-ef2a3b7f552b · outbound

This paper cites An end-to-end computer vision pipeline for automated cardiac function assessment by echocardiography,.

Survey on Deep Neural Networks in Speech and Vision Systems An end-to-end computer vision pipeline for automated cardiac function assessment by echocardiography,

Reference 14

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Observation 565e380b-daee-4c11-9108-c4f421219e14 · outbound

This paper cites A review of smart homes—Past, present, and future,.

Survey on Deep Neural Networks in Speech and Vision Systems A review of smart homes—Past, present, and future,

Reference 15

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Survey on Deep Neural Networks in Speech and Vision Systems Personal virtual assistant,

Reference 16

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Observation 8effee34-4ad6-4635-be05-790a4e0f6940 · outbound

This paper cites Application of data mining techniques in customer relationship management: A literature review and classification,.

Survey on Deep Neural Networks in Speech and Vision Systems Application of data mining techniques in customer relationship management: A literature review and classification,

Reference 17

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Survey on Deep Neural Networks in Speech and Vision Systems A review on application of data mining techniques to combat natural disasters,

Reference 18

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Observation 28be8827-fa62-4b08-a8ef-00f990f63f45 · outbound

This paper cites Vision based hand gesture recognition for human computer interaction: a survey,.

Survey on Deep Neural Networks in Speech and Vision Systems Vision based hand gesture recognition for human computer interaction: a survey,

Reference 19

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Observation ab10b8f9-204f-4adc-9875-c26cc8fede87 · outbound

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Survey on Deep Neural Networks in Speech and Vision Systems Deeppose: Human pose estimation via deep neural networks,

Reference 20

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Survey on Deep Neural Networks in Speech and Vision Systems Joint training of a convolutional network and a graphical model for human pose estimation,

Reference 21

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Survey on Deep Neural Networks in Speech and Vision Systems Safety and security in smart cities using artificial intelligence—A review,

Reference 22

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Observation 3164275e-c3a0-4939-94c2-e7122f84cec1 · outbound

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Survey on Deep Neural Networks in Speech and Vision Systems Detecting depression severity from vocal prosody,

Reference 23

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Observation b98bc186-c8ee-4ce7-9eea-fa73127929ba · outbound

This paper cites Speech and prosody characteristics of adolescents and adults with high-functioning autism and Asperger syndrome,.

Survey on Deep Neural Networks in Speech and Vision Systems Speech and prosody characteristics of adolescents and adults with high-functioning autism and Asperger syndrome,

Reference 24

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Observation 87f4e4ef-77bd-4df2-be33-b8d2a73d3761 · outbound

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Survey on Deep Neural Networks in Speech and Vision Systems Survey on speech emotion recognition: Features, classification schemes, and databases,

Reference 25

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Survey on Deep Neural Networks in Speech and Vision Systems Evaluating deep learning architectures for Speech Emotion Recognition,

Reference 26

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Survey on Deep Neural Networks in Speech and Vision Systems Deep learning for robust feature generation in audiovisual emotion recognition,

Reference 27

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Survey on Deep Neural Networks in Speech and Vision Systems A fast learning algorithm for deep belief nets,

Reference 28

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Survey on Deep Neural Networks in Speech and Vision Systems Learning multiple layers of representation,

Reference 29

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This paper cites Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence,.

Survey on Deep Neural Networks in Speech and Vision Systems Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence,

Reference 30

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Survey on Deep Neural Networks in Speech and Vision Systems Deep hierarchies in the primate visual cortex: What can we learn for computer vision?,

Reference 31

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Survey on Deep Neural Networks in Speech and Vision Systems Deep learning in neural networks: An overview,

Reference 32

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Survey on Deep Neural Networks in Speech and Vision Systems Imagenet classification with deep convolutional neural networks,

Reference 33

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Observation f4aac34f-6efa-4123-8813-dee70fbf2e7f · outbound

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Survey on Deep Neural Networks in Speech and Vision Systems Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,

Reference 34

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Survey on Deep Neural Networks in Speech and Vision Systems Generative adversarial nets,

Reference 35

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Observation bebd5e0f-1e50-4705-a1b7-d94c8ba72204 · outbound

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Survey on Deep Neural Networks in Speech and Vision Systems Auto-Encoding Variational Bayes

Reference 36

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Survey on Deep Neural Networks in Speech and Vision Systems Density estimation using Real NVP

Reference 37

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Observation 98d53d6c-b491-44ca-a39c-c4b46bb7734c · outbound

This paper cites A Critical Review of Recurrent Neural Networks for Sequence Learning.

Survey on Deep Neural Networks in Speech and Vision Systems A Critical Review of Recurrent Neural Networks for Sequence Learning

Reference 38

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Observation 0b63cf57-7806-47df-b37b-241922040c13 · outbound

This paper cites Attention is all you need,.

Survey on Deep Neural Networks in Speech and Vision Systems Attention is all you need,

Reference 39

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Observation 6854baa9-262b-421e-948c-93aad74e22fe · outbound

This paper cites Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection,.

Survey on Deep Neural Networks in Speech and Vision Systems Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection,

Reference 40

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Observation 9703dc47-7852-4c32-9190-4ee9cb2fcc09 · outbound

This paper cites Restricted Boltzmann machines for collaborative filtering,.

Survey on Deep Neural Networks in Speech and Vision Systems Restricted Boltzmann machines for collaborative filtering,

Reference 41

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Observation 10b34afc-3bfe-440b-9127-a587cc236667 · outbound

This paper cites Deep boltzmann machines,.

Survey on Deep Neural Networks in Speech and Vision Systems Deep boltzmann machines,

Reference 42

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Observation 18ce4847-0689-401e-99f6-70aca7fdcbe8 · outbound

This paper cites Extracting deep bottleneck features using stacked auto-encoders,.

Survey on Deep Neural Networks in Speech and Vision Systems Extracting deep bottleneck features using stacked auto-encoders,

Reference 43

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Observation f42abfe2-e355-49a7-b038-c7ccd78440ef · outbound

This paper cites Extracting and composing robust features with denoising autoencoders,.

Survey on Deep Neural Networks in Speech and Vision Systems Extracting and composing robust features with denoising autoencoders,

Reference 44

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Observation 81edcbb3-35e2-4335-a3b8-489f3ce5d2b5 · outbound

This paper cites Learning hierarchical representations for face verification with convolutional deep belief networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Learning hierarchical representations for face verification with convolutional deep belief networks,

Reference 45

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Observation bb3d104d-5db1-45da-9bbf-a34c0cf08ab3 · outbound

This paper cites Investigation of deep boltzmann machines for phone recognition,.

Survey on Deep Neural Networks in Speech and Vision Systems Investigation of deep boltzmann machines for phone recognition,

Reference 46

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Observation 0c230678-7e24-4780-ac80-4fa256350fbc · outbound

This paper cites Random Deep Belief Networks for Recognizing Emotions from Speech Signals,.

Survey on Deep Neural Networks in Speech and Vision Systems Random Deep Belief Networks for Recognizing Emotions from Speech Signals,

Reference 47

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Observation 98c97ae5-df14-445a-9e73-37026f2bda07 · outbound

This paper cites A research of speech emotion recognition based on deep belief network and SVM,.

Survey on Deep Neural Networks in Speech and Vision Systems A research of speech emotion recognition based on deep belief network and SVM,

Reference 48

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Observation b968b7c6-7da1-41d7-8119-a2f814ef4521 · outbound

This paper cites Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations,.

Survey on Deep Neural Networks in Speech and Vision Systems Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations,

Reference 49

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Observation 9bb0f783-0f21-4b1b-88c6-6590654d45d8 · outbound

This paper cites Attribute2image: Conditional image generation from visual attributes,.

Survey on Deep Neural Networks in Speech and Vision Systems Attribute2image: Conditional image generation from visual attributes,

Reference 50

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Observation 4c18870d-f363-4e90-8b05-73f1ab7f9ea8 · outbound

This paper cites An uncertain future: Forecasting from static images using variational autoencoders,.

Survey on Deep Neural Networks in Speech and Vision Systems An uncertain future: Forecasting from static images using variational autoencoders,

Reference 51

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Observation 56ae4670-c184-4684-9ac8-16e755a53e74 · outbound

This paper cites A Hybrid Convolutional Variational Autoencoder for Text Generation.

Survey on Deep Neural Networks in Speech and Vision Systems A Hybrid Convolutional Variational Autoencoder for Text Generation

Reference 52

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Observation e7f2dce6-2710-41a8-a792-6304c87001ce · outbound

This paper cites Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder.

Survey on Deep Neural Networks in Speech and Vision Systems Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder

Reference 53

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Observation dc320539-c920-44b7-a109-57aa2996b697 · outbound

This paper cites Generative Adversarial Text to Image Synthesis.

Survey on Deep Neural Networks in Speech and Vision Systems Generative Adversarial Text to Image Synthesis

Reference 54

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Observation 29b2fc7b-0fe0-41c6-a9ad-3eaac2c2031b · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

Survey on Deep Neural Networks in Speech and Vision Systems Photo-realistic single image super-resolution using a generative adversarial network,

Reference 55

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Observation 0cab5ea6-c721-4c3b-a308-b1d450a0f7a6 · outbound

This paper cites Conditional Generative Adversarial Nets.

Survey on Deep Neural Networks in Speech and Vision Systems Conditional Generative Adversarial Nets

Reference 56

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Observation bbcd27c7-99cf-4eb5-bd8a-9bf9b3dfdfe3 · outbound

This paper cites High-resolution image synthesis and semantic manipulation with conditional gans,.

Survey on Deep Neural Networks in Speech and Vision Systems High-resolution image synthesis and semantic manipulation with conditional gans,

Reference 57

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Observation d5718e79-4e4a-440b-99fe-401cfea24ea8 · outbound

This paper cites Adversarial Feature Learning.

Survey on Deep Neural Networks in Speech and Vision Systems Adversarial Feature Learning

Reference 58

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Observation a5890251-a72e-4421-9450-f61869b2fe23 · outbound

This paper cites Large scale adversarial representation learning,.

Survey on Deep Neural Networks in Speech and Vision Systems Large scale adversarial representation learning,

Reference 59

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Observation a6eb334d-9741-4c1d-8b13-6f30ea70d02f · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

Survey on Deep Neural Networks in Speech and Vision Systems Towards Principled Methods for Training Generative Adversarial Networks

Reference 60

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Observation 8575c3e9-d130-4754-a87d-5633453b58d8 · outbound

This paper cites NIPS 2016 Tutorial: Generative Adversarial Networks.

Survey on Deep Neural Networks in Speech and Vision Systems NIPS 2016 Tutorial: Generative Adversarial Networks

Reference 61

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Observation 500abceb-c6aa-4beb-afed-d73b8ac0818a · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Survey on Deep Neural Networks in Speech and Vision Systems Spectral Normalization for Generative Adversarial Networks

Reference 62

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Observation a9036aad-c42e-4806-94fb-7199bcc2ae7d · outbound

This paper cites Wasserstein generative adversarial networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Wasserstein generative adversarial networks,

Reference 63

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Observation c994c929-ad98-40f9-88d4-ec70dfdc02b7 · outbound

This paper cites Improved training of wasserstein gans,.

Survey on Deep Neural Networks in Speech and Vision Systems Improved training of wasserstein gans,

Reference 64

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Observation 79182df8-cf52-40f5-b9a4-bb583e18315b · outbound

This paper cites Least squares generative adversarial networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Least squares generative adversarial networks,

Reference 65

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Observation 9a79f556-6589-4605-bf88-4fc5de7da72d · outbound

This paper cites Generating Diverse High-Fidelity Images with VQ-VAE-2.

Survey on Deep Neural Networks in Speech and Vision Systems Generating Diverse High-Fidelity Images with VQ-VAE-2

Reference 66

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Observation dea243d5-3f79-4241-a8bd-419e5ae243a6 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Survey on Deep Neural Networks in Speech and Vision Systems NICE: Non-linear Independent Components Estimation

Reference 67

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Observation b95d918b-9ed1-4163-98ab-491c81e456df · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions,.

Survey on Deep Neural Networks in Speech and Vision Systems Glow: Generative flow with invertible 1x1 convolutions,

Reference 68

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Observation 06f58d48-413d-402c-8233-44f33ec99c5b · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Survey on Deep Neural Networks in Speech and Vision Systems WaveNet: A Generative Model for Raw Audio

Reference 69

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Observation 9714b967-d309-4423-8166-3f10e6571419 · outbound

This paper cites Pixel Recurrent Neural Networks.

Survey on Deep Neural Networks in Speech and Vision Systems Pixel Recurrent Neural Networks

Reference 70

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Observation 4d1b015d-be2b-4a83-9b78-eef6fe27badd · outbound

This paper cites Waveglow: A flow-based generative network for speech synthesis,.

Survey on Deep Neural Networks in Speech and Vision Systems Waveglow: A flow-based generative network for speech synthesis,

Reference 71

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Observation 6a817db0-8ef7-483e-bea8-1b9d860f057b · outbound

This paper cites SEGAN: Speech Enhancement Generative Adversarial Network.

Survey on Deep Neural Networks in Speech and Vision Systems SEGAN: Speech Enhancement Generative Adversarial Network

Reference 72

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Observation 8ef72377-b71c-440f-8281-9b4d90a7b94a · outbound

This paper cites Speech Enhancement for Noise-Robust Speech Synthesis Using Wasserstein GAN}},.

Survey on Deep Neural Networks in Speech and Vision Systems Speech Enhancement for Noise-Robust Speech Synthesis Using Wasserstein GAN}},

Reference 73

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Observation de37cad0-b38e-4af5-af52-67815a39a275 · outbound

This paper cites End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.

Survey on Deep Neural Networks in Speech and Vision Systems End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Reference 74

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Observation c620afa6-541e-4e5a-8189-cf7db4ba98e6 · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Survey on Deep Neural Networks in Speech and Vision Systems On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 75

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Observation e2e1dc73-e61e-493a-8729-5d3634c58e3a · outbound

This paper cites LSTM: A search space odyssey,.

Survey on Deep Neural Networks in Speech and Vision Systems LSTM: A search space odyssey,

Reference 76

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Observation c565236c-97f2-475a-b08d-0ba358b7f87f · outbound

This paper cites Recurrent models of visual attention,.

Survey on Deep Neural Networks in Speech and Vision Systems Recurrent models of visual attention,

Reference 77

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Observation 165303af-f1c3-46cb-9eac-cadcd52d6e2b · outbound

This paper cites Learning to combine foveal glimpses with a third-order Boltzmann machine,.

Survey on Deep Neural Networks in Speech and Vision Systems Learning to combine foveal glimpses with a third-order Boltzmann machine,

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Observation fa1bc016-cacc-4c93-a344-f45248c361b8 · outbound

This paper cites On Learning Where To Look.

Survey on Deep Neural Networks in Speech and Vision Systems On Learning Where To Look

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local_arxiv, observed 2026-08-14T13:01:12.744077Z

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Observation 6b5d8d92-cbb3-49cb-afcb-68a6ca2fba82 · outbound

This paper cites Learning where to attend with deep architectures for image tracking,.

Survey on Deep Neural Networks in Speech and Vision Systems Learning where to attend with deep architectures for image tracking,

Reference 80

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Observation 033eff53-4e1f-4af0-b159-196ff7d4c4be · outbound

This paper cites DRAW: A Recurrent Neural Network For Image Generation.

Survey on Deep Neural Networks in Speech and Vision Systems DRAW: A Recurrent Neural Network For Image Generation

Reference 81

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Observation 77c4d083-049c-4154-87d2-18dae9787c4e · outbound

This paper cites Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts,.

Survey on Deep Neural Networks in Speech and Vision Systems Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts,

Reference 82

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Observation 31aa1fdd-2095-4eea-a398-0cc198c395fa · outbound

This paper cites Generating Images from Captions with Attention.

Survey on Deep Neural Networks in Speech and Vision Systems Generating Images from Captions with Attention

Reference 83

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Observation a45ec348-dc86-42ef-955f-c08dddb5ba1f · outbound

This paper cites Neural Turing Machines.

Survey on Deep Neural Networks in Speech and Vision Systems Neural Turing Machines

Reference 84

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source=pdf_text observed=2026-08-14T13:01:09.709113Z digest=sha256:830cd15b70549550378fff14307c698006075a49bdb76794ac09eaa033ad0d92

Observation e2b12437-d4e7-43f0-912a-907fb2f020bc · outbound

This paper cites Effective Approaches to Attention-based Neural Machine Translation.

Survey on Deep Neural Networks in Speech and Vision Systems Effective Approaches to Attention-based Neural Machine Translation

Reference 85

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Observation e19fbff3-82e8-4bc5-a43c-a000878bc251 · outbound

This paper cites Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,.

Survey on Deep Neural Networks in Speech and Vision Systems Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,

Reference 86

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Observation 02618946-b16a-4dc0-bcca-59b3fbb0f9a3 · outbound

This paper cites Skeleton-based action recognition using spatio-temporal LSTM network with trust gates,.

Survey on Deep Neural Networks in Speech and Vision Systems Skeleton-based action recognition using spatio-temporal LSTM network with trust gates,

Reference 87

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Observation 2a771925-8704-44ce-ba07-701759d77e02 · outbound

This paper cites Self-Attention Generative Adversarial Networks.

Survey on Deep Neural Networks in Speech and Vision Systems Self-Attention Generative Adversarial Networks

Reference 88

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Observation e6b92fbd-bdf7-4c26-9741-d3292038e5ca · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Survey on Deep Neural Networks in Speech and Vision Systems Neural Architecture Search with Reinforcement Learning

Reference 89

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Observation 391ea093-95bc-44d6-a1f0-265642c2c07e · outbound

This paper cites DARTS: Differentiable Architecture Search.

Survey on Deep Neural Networks in Speech and Vision Systems DARTS: Differentiable Architecture Search

Reference 90

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Observation 56bba1c7-faf8-44b1-b83a-9966221dfa55 · outbound

This paper cites Progressive neural architecture search,.

Survey on Deep Neural Networks in Speech and Vision Systems Progressive neural architecture search,

Reference 91

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Observation b8331225-fd2a-4791-9e12-a7972010132e · outbound

This paper cites Learning hierarchical features for scene labeling,.

Survey on Deep Neural Networks in Speech and Vision Systems Learning hierarchical features for scene labeling,

Reference 92

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source=pdf_text observed=2026-08-14T13:01:09.792646Z digest=sha256:e2e4de1088e1b7de836c18e1d9bc6598cce43300b2e2cefdce21af3f26de9581

Observation aec87521-37eb-4fb7-9f73-0accffae8363 · outbound

This paper cites Going deeper with convolutions,.

Survey on Deep Neural Networks in Speech and Vision Systems Going deeper with convolutions,

Reference 93

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Observation 2c737217-3d2b-4c75-8deb-46f0547af6d4 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Survey on Deep Neural Networks in Speech and Vision Systems Imagenet large scale visual recognition challenge,

Reference 94

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Observation 1886ac6a-668f-4324-9636-d384629ebd32 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Survey on Deep Neural Networks in Speech and Vision Systems Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 95

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source=pdf_text observed=2026-08-14T13:01:09.832851Z digest=sha256:bc1123725044ed7e81ed0567ed6e0ebbe3772fbf0ed68a065b3ec9c027fccb2e

Observation ac9c22a0-d85a-4451-b3fd-915fe0c5fd21 · outbound

This paper cites Visualizing and understanding convolutional networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Visualizing and understanding convolutional networks,

Reference 96

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Observation b2b01d53-6e2e-4d7a-8940-85fade305c65 · outbound

This paper cites Wider or Deeper: Revisiting the ResNet Model for Visual Recognition.

Survey on Deep Neural Networks in Speech and Vision Systems Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

Reference 97

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Observation 1dd5cb84-eae1-4d0f-ae2d-ddcaaa62127d · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Survey on Deep Neural Networks in Speech and Vision Systems Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 98

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Observation bf00056b-1b0d-47a4-8240-1e5286212507 · outbound

This paper cites Densely connected convolutional networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Densely connected convolutional networks,

Reference 99

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Observation 71464a5a-b82b-46f9-b4fa-80b85e596a41 · outbound

This paper cites Squeeze-and-excitation networks,.

Survey on Deep Neural Networks in Speech and Vision Systems Squeeze-and-excitation networks,

Reference 100

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Pith citing papers

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