TPCNet: Representation learning for HI mapping | Paper Citation Record

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TPCNet: Representation learning for HI mapping write newline

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TPCNet: Representation learning for HI mapping Unresolved cited work

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This paper cites Attention Augmented Convolutional Networks.

TPCNet: Representation learning for HI mapping Attention Augmented Convolutional Networks

Reference 6

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This paper cites Representation Learning: A Review and New Perspectives.

TPCNet: Representation learning for HI mapping Representation Learning: A Review and New Perspectives

Reference 7

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

TPCNet: Representation learning for HI mapping Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 8

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This paper cites End-to-End Object Detection with Transformers.

TPCNet: Representation learning for HI mapping End-to-End Object Detection with Transformers

Reference 9

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This paper cites Unsupervised Learning of Visual Features by Contrasting Cluster Assignments.

TPCNet: Representation learning for HI mapping Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Reference 10

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This paper cites A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937.

TPCNet: Representation learning for HI mapping A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

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This paper cites H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827.

TPCNet: Representation learning for HI mapping H., Appleby S., 2019, @doi [ ] 10.1093/mnras/stz937 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2827D 486, 2827

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TPCNet: Representation learning for HI mapping Universal Transformers

Reference 13

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TPCNet: Representation learning for HI mapping M., Dickey J

Reference 14

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

TPCNet: Representation learning for HI mapping BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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TPCNet: Representation learning for HI mapping M., Lockman F

Reference 16

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TPCNet: Representation learning for HI mapping M., Salpeter E

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TPCNet: Representation learning for HI mapping M., McClure-Griffiths N

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TPCNet: Representation learning for HI mapping M., Strasser S., Gaensler B

Reference 19

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

TPCNet: Representation learning for HI mapping An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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This paper cites Position Information in Transformers: An Overview.

TPCNet: Representation learning for HI mapping Position Information in Transformers: An Overview

Reference 21

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TPCNet: Representation learning for HI mapping Unresolved cited work

Reference 22

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TPCNet: Representation learning for HI mapping B., 1965, @doi [ ] 10.1086/148317 , https://ui.adsabs.harvard.edu/abs/1965ApJ...142..531F 142, 531

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TPCNet: Representation learning for HI mapping B., Goldsmith D

Reference 24

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TPCNet: Representation learning for HI mapping J., Scalo J., 2001, @doi [ ] 10.1086/322873 , http://adsabs.harvard.edu/abs/2001ApJ...557L.121G 557, L121

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This paper cites Convolutional Sequence to Sequence Learning.

TPCNet: Representation learning for HI mapping Convolutional Sequence to Sequence Learning

Reference 27

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TPCNet: Representation learning for HI mapping Unresolved cited work

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TPCNet: Representation learning for HI mapping C., Kim C.-G., 2018, @doi [ ] 10.3847/1538-4357/aab9af , https://ui.adsabs.harvard.edu/abs/2018ApJ...858...16G 858, 16

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TPCNet: Representation learning for HI mapping C., Kim C.-G., Kim J.-G., 2020, @doi [ ] 10.3847/1538-4357/abbdab , https://ui.adsabs.harvard.edu/abs/2020ApJ...903..142G 903, 142

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TPCNet: Representation learning for HI mapping MIT Press

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TPCNet: Representation learning for HI mapping Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 32

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TPCNet: Representation learning for HI mapping ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

Reference 34

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TPCNet: Representation learning for HI mapping H., 2003a, @doi [ ] 10.1086/367785 , http://adsabs.harvard.edu/abs/2003ApJS..145..329H 145, 329

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TPCNet: Representation learning for HI mapping H., 2003b, @doi [ ] 10.1086/367828 , http://adsabs.harvard.edu/abs/2003ApJ...586.1067H 586, 1067

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TPCNet: Representation learning for HI mapping S., Murray C

Reference 39

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TPCNet: Representation learning for HI mapping S., Mac Low M.-M., Gatto A., Ib \'a \ n ez-Mej \' a J

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TPCNet: Representation learning for HI mapping D., Krumholz M

Reference 41

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TPCNet: Representation learning for HI mapping D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

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TPCNet: Representation learning for HI mapping Rethinking Positional Encoding in Language Pre-training

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TPCNet: Representation learning for HI mapping Transformers in Vision: A Survey

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TPCNet: Representation learning for HI mapping C., 2017, @doi [ ] 10.3847/1538-4357/aa8599 , https://ui.adsabs.harvard.edu/abs/2017ApJ...846..133K 846, 133

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TPCNet: Representation learning for HI mapping C., Kim W.-T., 2013, @doi [ ] 10.1088/0004-637X/776/1/1 , http://adsabs.harvard.edu/abs/2013ApJ...776....1K 776, 1

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TPCNet: Representation learning for HI mapping C., 2020, @doi [ ] 10.3847/1538-4357/ab9b87 , https://ui.adsabs.harvard.edu/abs/2020ApJ...898...35K 898, 35

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TPCNet: Representation learning for HI mapping C., 2023, @doi [ ] 10.3847/1538-4357/acbd3a , https://ui.adsabs.harvard.edu/abs/2023ApJ...946....3K 946, 3

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TPCNet: Representation learning for HI mapping Reformer: The Efficient Transformer

Reference 51

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TPCNet: Representation learning for HI mapping QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions

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TPCNet: Representation learning for HI mapping R., McKee C

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TPCNet: Representation learning for HI mapping R., Heiles C., 1988, Neutral hydrogen and the diffuse interstellar medium

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TPCNet: Representation learning for HI mapping ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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TPCNet: Representation learning for HI mapping E., Heiles C., Miller J., 2015, @doi [ ] 10.1088/0004-637X/809/1/56 , http://adsabs.harvard.edu/abs/2015ApJ...809...56L 809, 56

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This paper cites A New Constraint on the Relative Disorder of Magnetic Fields between Neutral Interstellar Medium Phases.

TPCNet: Representation learning for HI mapping A New Constraint on the Relative Disorder of Magnetic Fields between Neutral Interstellar Medium Phases

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Reference 64

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Reference 65

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Reference 66

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Reference 68

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Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 76

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Reference 77

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Reference 79

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Reference 80

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Reference 81

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TPCNet: Representation learning for HI mapping Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer

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Reference 84

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Reference 85

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Reference 86

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TPCNet: Representation learning for HI mapping Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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TPCNet: Representation learning for HI mapping Toward a Spectral Foundation Model: An Attention-Based Approach with Domain-Inspired Fine-Tuning and Wavelength Parameterization

Reference 90

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Reference 91

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Reference 92

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Reference 96

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Pith citing papers