Fetching the paper…
Reading the bibliography…
Data augmentation plays a key role in modern machine learning pipelines.
The statistical analysis of compositional data
J. Aitchison · 1982
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Compositional data analysis and zeros in micro data
J. M. Fry, T. R. Fry, and K. R. McLaren · 2000
Earlier work this paper cites.
Compositional data analysis in the geosciences: from theory to practice
A. Buccianti, G. Mateu-Figueras, and V. Pawlowsky-Glahn · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
Earlier work this paper cites.
Lecture notes on compositional data analysis
V. Pawlowsky-Glahn, J. J. Egozcue, and R. Tolosana Delgado · 2007
Earlier work this paper cites.
The human microbiome project
P. J. Turnbaugh, R. E. Ley, M. Hamady, C. M. Fraser-Liggett, R. Knight, and J. I. Gordon · 2007
Earlier work this paper cites.
Compositional data analysis: Theory and applications
V. Pawlowsky-Glahn and A. Buccianti · 2011
Earlier work this paper cites.
Vaginal microbiome of reproductive-age women
J. Ravel, P. Gajer, Z. Abdo, G. M. Schneider, S. S. Koenig, S. L. McCulle, S. Karlebach, R. Gorle, J. Russell, C. O. Tacket, et al · 2011
Earlier work this paper cites.
Genomic analysis identifies association of fusobacterium with colorectal carcinoma
A. D. Kostic, D. Gevers, C. S. Pedamallu, M. Michaud, F. Duke, A. M. Earl, A. I. Ojesina, J. Jung, A. J. Bass, J. Tabernero, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
A framework for human microbiome research
B. A. Methé, K. E. Nelson, M. Pop, H. H. Creasy, M. G. Giglio, C. Huttenhower, D. Gevers, J. F. Petrosino, S. Abubucker, J. H. Badger, et al · 2012
Earlier work this paper cites.
A metagenome-wide association study of gut microbiota in type 2 diabetes
J. Qin, Y. Li, Z. Cai, S. Li, J. Zhu, F. Zhang, S. Liang, W. Zhang, Y. Guan, D. Shen, et al · 2012
Earlier work this paper cites.
Compositional data analysis in geochemistry: Are we sure to see what really occurs during natural processes?, 2014
A. Buccianti and E. Grunsky · 2014
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
Earlier work this paper cites.
The treatment-naive microbiome in new-onset crohn’s disease
D. Gevers, S. Kugathasan, L. A. Denson, Y. Vázquez-Baeza, W. Van Treuren, B. Ren, E. Schwager, D. Knights, S. J. Song, M. Yassour, et al · 2014
Earlier work this paper cites.
Compositional landscape for glass formation in metal alloys
J. H. Na, M. D. Demetriou, M. Floyd, A. Hoff, G. R. Garrett, and W. L. Johnson · 2014
Earlier work this paper cites.
Alterations of the human gut microbiome in liver cirrhosis
N. Qin, F. Yang, A. Li, E. Prifti, Y. Chen, L. Shao, J. Guo, E. Le Chatelier, J. Yao, L. Wu, et al · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Insights into the role of the microbiome in obesity and type 2 diabetes
A. V. Hartstra, K. E. Bouter, F. Bäckhed, and M. Nieuwdorp · 2015
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
R. Sennrich, B. Haddow, and A. Birch · 2015
Earlier work this paper cites.
Recent advances in characterizing the gastrointestinal microbiome in crohn’s disease: a systematic review
E. K. Wright, M. A. Kamm, S. M. Teo, M. Inouye, J. Wagner, and C. D. Kirkwood · 2015
Cited alongside, same era.
Worldwide burden of colorectal cancer: a review
P. Favoriti, G. Carbone, M. Greco, F. Pirozzi, R. E. M. Pirozzi, and F. Corcione · 2016
Cited alongside, same era.
Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data
G. B. Gloor and G. Reid · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
Cited alongside, same era.
Gut microbiome, big data and machine learning to promote precision medicine for cancer
G. Cammarota, G. Ianiro, A. Ahern, C. Carbone, A. Temko, M. J. Claesson, A. Gasbarrini, and G. Tortora · 2020
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Later among the works it cites.
The continuous categorical: a novel simplex-valued exponential family
E. Gordon-Rodriguez, G. Loaiza-Ganem, and J. Cunningham · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. B. Gloor, J. M. Macklaim, V. Pawlowsky-Glahn, and J. J. Egozcue · 2017
Cited alongside, same era.
On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
L. Perez and J. Wang · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Cited alongside, same era.
Representation learning of compositional data
M. Avalos, R. Nock, C. S. Ong, J. Rouar, and K. Sun · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Cited alongside, same era.
E. Gordon-Rodriguez, G. Loaiza-Ganem, G. Pleiss, and J. P. Cunningham · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
Later among the works it cites.
Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
Later among the works it cites.
Deepmicro: deep representation learning for disease prediction based on microbiome data
M. Oh and L. Zhang · 2020
Later among the works it cites.
Deepcoda: personalized interpretability for compositional health data
T. Quinn, D. Nguyen, S. Rana, S. Gupta, and S. Venkatesh · 2020
Later among the works it cites.
A framework for effective application of machine learning to microbiome-based classification problems
B. D. Topçuoğlu, N. A. Lesniak, M. T. Ruffin IV, J. Wiens, and P. D. Schloss · 2020
Later among the works it cites.
maml: an automated machine learning pipeline with a microbiome repository for human disease classification
F. Yang and Q. Zou · 2020
Later among the works it cites.
Vime: Extending the success of self-and semi-supervised learning to tabular domain
J. Yoon, Y. Zhang, J. Jordon, and M. van der Schaar · 2020
Later among the works it cites.
A survey of data augmentation approaches for nlp
S. Y. Feng, V. Gangal, J. Wei, S. Chandar, S. Vosoughi, T. Mitamura, and E. Hovy · 2021
Later among the works it cites.
Automl: A survey of the state-of-the-art
X. He, K. Zhao, and X. Chu · 2021
Later among the works it cites.
A practical guide to amplicon and metagenomic analysis of microbiome data
Y.-X. Liu, Y. Qin, T. Chen, M. Lu, X. Qian, X. Guo, and Y. Bai · 2021
Later among the works it cites.
Feature extension of gut microbiome data for deep neural network-based colorectal cancer classification
M. Mulenga, S. A. Kareem, A. Q. M. Sabri, M. Seera, S. Govind, C. Samudi, and S. B. Mohamad · 2021
Later among the works it cites.
A critique of differential abundance analysis, and advocacy for an alternative
T. P. Quinn, E. Gordon-Rodriguez, and I. Erb · 2021
Later among the works it cites.
On the normalizing constant of the continuous categorical distribution
E. Gordon-Rodriguez, G. Loaiza-Ganem, A. Potapczynski, and J. P. Cunningham · 2022
Closest in time.
Learning sparse log-ratios for high-throughput sequencing data
E. Gordon-Rodriguez, T. P. Quinn, and J. P. Cunningham · 2022
Closest in time.