Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Major publications by the team in recent years
[1]
A. Mensch, J. Mairal, B. Thirion, G. Varoquaux.
Stochastic Subsampling for Factorizing Huge Matrices, in: IEEE Transactions on Signal Processing, January 2018, vol. 66, no 1, pp. 113-128. [ DOI : 10.1109/TSP.2017.2752697 ]
https://hal.archives-ouvertes.fr/hal-01431618
Publications of the year

Doctoral Dissertations and Habilitation Theses

[2]
M. Massias.
Sparse high dimensional regression in the presence of colored heteroscedastic noise: application to M/EEG source imaging, Telecom Paristech, December 2019.
https://tel.archives-ouvertes.fr/tel-02401628

Articles in International Peer-Reviewed Journals

[3]
D. Bzdok, D. Floris, A. Marquand.
Analyzing Brain Networks in Population Neuroscience: A Case for the Bayesian Philosophy, in: Philosophical Transactions of the Royal Society of London. B (1887–1895), January 2020.
https://hal.archives-ouvertes.fr/hal-02447507
[4]
D. Bzdok, J. P. A. Ioannidis.
Exploration, inference and prediction in neuroscience and biomedicine, in: Trends in Neurosciences, March 2019, https://arxiv.org/abs/1903.10310. [ DOI : 10.1016/j.tins.2019.02.001 ]
https://hal.archives-ouvertes.fr/hal-02044120
[5]
D. Bzdok, T. E. Nichols, S. Smith.
Towards Algorithmic Analytics for Large-scale Datasets, in: Nature Machine Intelligence, July 2019. [ DOI : 10.1038/s42256-019-0069-5 ]
https://hal.archives-ouvertes.fr/hal-02178410
[6]
S. Chambon, V. Thorey, P. J. Arnal, E. J. M. Mignot, A. Gramfort.
DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal, in: Journal of Neuroscience Methods, June 2019, vol. 321, pp. 64-78, https://arxiv.org/abs/1812.04079. [ DOI : 10.1016/j.jneumeth.2019.03.017 ]
https://hal.inria.fr/hal-02121090
[7]
L. Chen, D. Wassermann, D. Abrams, J. Kochalka, G. Gallardo-Diez, V. Menon.
The visual word form area (VWFA) is part of both language and attention circuitry, in: Nature Communications, December 2019, vol. 10, no 1, Lang Chen, Demian Wassermann, and Daniel Abrams contributed equally. [ DOI : 10.1038/s41467-019-13634-z ]
https://hal.inria.fr/hal-02401938
[8]
K. Dadi, M. Rahim, A. Abraham, D. Chyzhyk, M. Milham, B. Thirion, G. Varoquaux.
Benchmarking functional connectome-based predictive models for resting-state fMRI, in: NeuroImage, May 2019, no 192, pp. 115-134. [ DOI : 10.1016/j.neuroimage.2019.02.062 ]
https://hal.inria.fr/hal-01824205
[9]
R. H. Fick, D. Wassermann, R. Deriche.
The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy, in: Frontiers in Neuroinformatics, October 2019, vol. 13. [ DOI : 10.3389/fninf.2019.00064 ]
https://hal.archives-ouvertes.fr/hal-02400877
[10]
P. Filipiak, R. H. Fick, A. Petiet, M. Santin, A.-C. Philippe, S. Lehéricy, P. Ciuciu, R. Deriche, D. Wassermann.
Reducing the number of samples in spatiotemporal dMRI acquisition design, in: Magnetic Resonance in Medicine, 2019. [ DOI : 10.1002/mrm.27601 ]
https://hal.archives-ouvertes.fr/hal-01928734
[11]
T. He, R. Kong, A. J. Holmes, M. Q. Nguyen, M. Sabuncu, S. B. Eickhoff, D. Bzdok, J. Feng, B. T. Yeo.
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics, in: NeuroImage, October 2019, 116276 p. [ DOI : 10.1016/j.neuroimage.2019.116276 ]
https://hal.archives-ouvertes.fr/hal-02314718
[12]
M. Jording, D. Engemann, H. Eckert, G. Bente, K. Vogeley.
Distinguishing Social From Private Intentions Through the Passive Observation of Gaze Cues, in: Frontiers in Human Neuroscience, December 2019, vol. 13. [ DOI : 10.3389/fnhum.2019.00442 ]
https://hal.archives-ouvertes.fr/hal-02416981
[13]
T. M. Karrer, D. S. Bassett, B. Derntl, O. Gruber, A. Aleman, R. Jardri, A. R. Laird, P. Fox, S. B. Eickhoff, O. Grisel, G. Varoquaux, B. Thirion, D. Bzdok.
Brain-based ranking of cognitive domains to predict schizophrenia, in: Human Brain Mapping, July 2019. [ DOI : 10.1002/hbm.24716 ]
https://hal.archives-ouvertes.fr/hal-02180311
[14]
C. Lazarus, P. Weiss, N. Chauffert, F. Mauconduit, L. El Gueddari, C. Destrieux, I. Zemmoura, A. Vignaud, P. Ciuciu.
SPARKLING: variable-density k-space filling curves for accelerated T 2 * -weighted MRI, in: Magnetic Resonance in Medicine, June 2019, vol. 81, no 6, pp. 3643-3661. [ DOI : 10.1002/mrm.27678 ]
https://hal.inria.fr/hal-02361265
[15]
J. Lebenberg, J.-F. Mangin, B. Thirion, C. Poupon, L. Hertz-Pannier, F. Leroy, P. Adibpour, G. Dehaene-Lambertz, J. Dubois.
Mapping the asynchrony of cortical maturation in the infant brain: a MRI multi-parametric clustering approach, in: NeuroImage, January 2019, vol. 185, pp. 641-653. [ DOI : 10.1016/j.neuroimage.2018.07.022 ]
https://hal.archives-ouvertes.fr/hal-01966812
[16]
J. Li, T. Bolt, D. Bzdok, J. S. Nomi, B. T. Yeo, R. Nathan Spreng, L. Q. Uddin.
Topography and Behavioral Relevance of the Global Signal in the Human Brain, in: Scientific Reports, October 2019, vol. 9, no 1. [ DOI : 10.1038/s41598-019-50750-8 ]
https://hal.archives-ouvertes.fr/hal-02305188
[17]
G. Nguyen, N. Aunai, D. Fontaine, E. Le Pennec, J. Van den Bossche, A. Jeandet, B. Bakkali, L. Vignoli, B. Regaldo-Saint Blancard.
Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, in: The Astrophysical journal letters, April 2019, vol. 874, no 2, 145 p, https://arxiv.org/abs/1903.10780. [ DOI : 10.3847/1538-4357/ab0d24 ]
https://hal.sorbonne-universite.fr/hal-02103805
[18]
V.-D. Nguyen, M. de Leoni, T. Dancheva, J. Jansson, J. Hoffman, D. Wassermann, J.-R. Li.
Portable simulation framework for diffusion MRI, in: Journal of Magnetic Resonance, December 2019, vol. 309, 106611 p. [ DOI : 10.1016/j.jmr.2019.106611 ]
https://hal.archives-ouvertes.fr/hal-02431598
[19]
L. J. O'Donnell, A. Daducci, D. Wassermann, C. Lenglet.
Advances in computational and statistical diffusion MRI, in: NMR in Biomedicine, 2019, vol. 32, no 4, e3805 p. [ DOI : 10.1002/nbm.3805 ]
https://hal.inria.fr/hal-02432249
[20]
M. Rahim, B. Thirion, G. Varoquaux.
Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation, in: Medical Image Analysis, March 2019. [ DOI : 10.1016/j.media.2019.03.001 ]
https://hal.inria.fr/hal-02068389
[21]
S. Saremi, A. Hyvärinen.
Neural Empirical Bayes, in: Journal of Machine Learning Research, 2019, vol. 20, pp. 1 - 23.
https://hal.inria.fr/hal-02419496
[22]
R. Tomi-Tricot, V. Gras, B. Thirion, F. Mauconduit, N. Boulant, H. Cherkaoui, P. Zerbib, A. Vignaud, A. Luciani, A. Amadon.
SmartPulse, a Machine Learning Approach for Calibration-Free Dynamic RF Shimming: Preliminary Study in a Clinical Environment, in: Magnetic Resonance in Medicine, June 2019, vol. 82, pp. 2016–2031. [ DOI : 10.1002/mrm.27870 ]
https://hal-cea.archives-ouvertes.fr/cea-02141266
[23]
G. Varoquaux, R. Poldrack.
Predictive models avoid excessive reductionism in cognitive neuroimaging, in: Current Opinion in Neurobiology, April 2019, vol. 55, forthcoming. [ DOI : 10.1016/j.conb.2018.11.002 ]
https://hal.archives-ouvertes.fr/hal-01856412

International Conferences with Proceedings

[24]
P. Ablin, D. Fagot, H. Wendt, A. Gramfort, C. Févotte.
A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning, in: IEEE-ICASSP 2019 - International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019.
https://hal.archives-ouvertes.fr/hal-02346829
[25]
S. Aydöre, B. Thirion, G. Varoquaux.
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data, in: NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.
https://hal.inria.fr/hal-02318458
[26]
T. Bin Masood, J. Budin, M. Falk, G. Favelier, C. Garth, C. Gueunet, P. Guillou, L. Hofmann, P. Hristov, A. Kamakshidasan, C. Kappe, P. Klacansky, P. Laurin, J. A. Levine, J. Lukasczyk, D. Sakurai, M. Soler, P. Steneteg, J. Tierny, W. Usher, J. Vidal, M. Wozniak.
An Overview of the Topology ToolKit, in: TopoInVis 2019 - Topological Methods in Data Analysis and Visualization, Nykoping, Sweden, June 2019.
https://hal.archives-ouvertes.fr/hal-02159838
[27]
H. Cherkaoui, T. Moreau, A. Halimi, P. Ciuciu.
Sparsity-based blind deconvolution of neural activation signal in fMRI, in: IEEE-ICASSP 2019 - International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, May 2019.
https://hal.inria.fr/hal-02085810
[28]
O. D. Domingues, P. Ciuciu, D. La Rocca, P. Abry, H. Wendt.
Multifractal analysis for cumulant-based epileptic seizure detection in eeg time series, in: ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Venise, Italy, April 2019.
https://hal.inria.fr/hal-02108099
[29]
L. El Gueddari, P. Ciuciu, E. Chouzenoux, A. Vignaud, J.-C. Pesquet.
Calibrationless oscar-based image reconstruction in compressed sensing parallel MRI, in: ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Venise, Italy, April 2019.
https://hal.inria.fr/hal-02101262
[30]
P. Filipiak, R. Fick, A. Petiet, M. Santin, A.-C. Philippe, S. Lehéricy, R. Deriche, D. Wassermann.
Coarse-Grained Spatiotemporal Acquisition Design for Diffusion MRI, in: ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Venice, Italy, April 2019.
https://hal.inria.fr/hal-01973588
[31]
L. E. Gueddari, E. Chouzenoux, A. Vignaud, J.-C. Pesquet, P. Ciuciu.
Online MR image reconstruction for compressed sensing acquisition in T2* imaging, in: SPIE Conference - Wavelets and Sparsity XVIII, San Diego, United States, August 2019.
https://hal.inria.fr/hal-02265538
[32]
A. Halimi, P. Ciuciu, A. Mccarthy, S. Mclaughlin, G. S. Buller.
Fast adaptive scene sampling for single-photon 3D lidar images, in: IEEE CAMSAP 2019 - International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Le Gosier (Guadeloupe), France, December 2019.
https://hal.inria.fr/hal-02298998
[33]
H. Janati, T. Bazeille, B. Thirion, M. Cuturi, A. Gramfort.
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates, in: IPMI 2019 - 26th international conference on Information Processing in Medical Imaging, Hong Kong, Hong Kong SAR China, Springer (editor), June 2019, vol. Lecture Notes in Computer Science, https://arxiv.org/abs/1902.04812.
https://hal.inria.fr/hal-02013889
[34]
H. Janati, M. Cuturi, A. Gramfort.
Wasserstein regularization for sparse multi-task regression, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, April 2019, vol. 89.
https://hal.inria.fr/hal-02304176
[35]
Z. Ramzi, P. Ciuciu, J.-L. Starck.
Benchmarking proximal methods acceleration enhancements for CS-acquired MR image analysis reconstruction, in: SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations Workshop, Toulouse, France, July 2019.
https://hal.inria.fr/hal-02298569
[36]
Z. Ramzi, P. Ciuciu, J.-L. Starck.
Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset, in: ISBI 2020 - International Symposium on Biomedical Imaging, Iowa City, United States, April 2020.
https://hal.inria.fr/hal-02436223
[37]
M. Scetbon, G. Varoquaux.
Comparing distributions: l1 geometry improves kernel two-sample testing, in: NeurIPS 2019 - 33th Conference on Neural Information Processing Systems, Vancouver, Canada, 2019.
https://hal.inria.fr/hal-02292545

Conferences without Proceedings

[38]
P. Ablin, D. Fagot, H. Wendt, A. Gramfort, C. Févotte.
A quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning, in: IEEE-ICASSP 2019 - International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, May 2019, https://arxiv.org/abs/1811.02225.
https://hal.archives-ouvertes.fr/hal-01912918
[39]
P. Ablin, A. Gramfort, J.-F. Cardoso, F. Bach.
Stochastic algorithms with descent guarantees for ICA, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, April 2019.
https://hal.archives-ouvertes.fr/hal-02372092
[40]
T. Bazeille, H. Richard, H. Janati, B. Thirion.
Local Optimal Transport for Functional Brain Template Estimation, in: IPMI 2019 - 26th International Conference on Information Processing in Medical Imaging, Hong Kong, China, June 2019. [ DOI : 10.1007/978-3-030-20351-1_18 ]
https://hal.archives-ouvertes.fr/hal-02278663
[41]
H. Cherkaoui, T. Moreau, A. Halimi, P. Ciuciu.
fMRI BOLD signal decomposition using a multivariate low-rank model, in: Eusipco 2019 - 27th European Signal Processing Conference, Corunna, Spain, September 2019.
https://hal.archives-ouvertes.fr/hal-02163497
[42]
L. El Gueddari, P. Ciuciu, E. Chouzenoux, A. Vignaud, J.-C. Pesquet.
Online compressed sensing MR image reconstruction for high resolution T2* imaging, in: ISMRM 2019 - 27th Annual Meeting and Exhibition, Montréal, Canada, May 2019.
https://hal.archives-ouvertes.fr/hal-02314904
[43]
L. El Gueddari, P. Ciuciu, E. Chouzenoux, A. Vignaud, J.-C. Pesquet.
OSCAR-based reconstruction for compressed sensing and parallel MR imaging, in: ISMRM 2019 - 27th Annual Meeting and Exhibition, Montréal, Canada, May 2019.
https://hal.archives-ouvertes.fr/hal-02314911
[44]
C. Lazarus, P. Weiss, F. Mauconduit, A. Vignaud, P. Ciuciu.
3D SPARKLING for accelerated ex vivo T2*-weighted MRI with compressed sensing, in: ISMRM 2019 - 27th Annual Meeting & Exhibition, Montréal, France, May 2019.
https://hal.archives-ouvertes.fr/hal-02374538
[45]
M. Massias, S. Vaiter, A. Gramfort, J. Salmon.
Exploiting regularity in sparse Generalized Linear Models, in: SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations, Toulouse, France, July 2019.
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02288859
[46]
T.-B. Nguyen, J.-A. Chevalier, B. Thirion.
ECKO: Ensemble of Clustered Knockoffs for robust multivariate inference on MRI data, in: IPMI 2019 - International Conference on Information Processing in Medical Imaging, Hong Kong, Hong Kong SAR China, June 2019.
https://hal.archives-ouvertes.fr/hal-02076510
[47]
D. Sabbagh, P. Ablin, G. Varoquaux, A. Gramfort, D. Engemann.
Manifold-regression to predict from MEG/EEG brain signals without source modeling, in: NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.
https://hal.archives-ouvertes.fr/hal-02147708

Scientific Books (or Scientific Book chapters)

[48]
L. Esch, C. Dinh, E. Larson, D. Engemann, M. Jas, S. Khan, A. Gramfort, M. Hämäläinen.
MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data, in: Magnetoencephalography, Springer International Publishing, October 2019, pp. 355-371. [ DOI : 10.1007/978-3-030-00087-5_59 ]
https://hal.inria.fr/hal-02369299

Internal Reports

[49]
P. Ciuciu, A. Kazeykina.
Anisotropic compressed sensing for non-Cartesian MRI acquisitions, CEA Paris Saclay ; Inria ; Université Paris Sud, Université Paris Saclay, October 2019, https://arxiv.org/abs/1910.14513.
https://hal.archives-ouvertes.fr/hal-02339689

Other Publications

[50]
S. Abboud, D. Engemann, L. Cohen.
Semantic coding in the occipital cortex of early blind individuals, February 2019, working paper or preprint. [ DOI : 10.1101/539437 ]
https://hal.archives-ouvertes.fr/hal-02018272
[51]
P. Ablin, T. Moreau, M. Massias, A. Gramfort.
Learning step sizes for unfolded sparse coding, May 2019, working paper or preprint.
https://hal.inria.fr/hal-02140383
[52]
F. Baillin, A. Lefebvre, A. Pedoux, Y. Beauxis, D. Engemann, A. Maruani, F. Amsellem, T. Bourgeron, R. Delorme, G. Dumas.
Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains, January 2020, Literature reference for the preprint first posted on medRxiv: https://www.medrxiv.org/content/10.1101/19013771v1. [ DOI : 10.1101/19013771 ]
https://hal.inria.fr/hal-02396923
[53]
H. Banville, I. Albuquerque, A. Hyvärinen, G. Moffat, D. Engemann, A. Gramfort.
Self-supervised representation learning from electroencephalography signals, November 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02361350
[54]
Q. Bertrand, M. Massias, A. Gramfort, J. Salmon.
Handling correlated and repeated measurements with the smoothed Multivariate square-root Lasso, September 2019, https://arxiv.org/abs/1902.02509 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02010014
[55]
P. Cerda, G. Varoquaux.
Encoding high-cardinality string categorical variables, July 2019, https://arxiv.org/abs/1907.01860 - working paper or preprint.
https://hal.inria.fr/hal-02171256
[56]
L. El Gueddari, E. Chouzenoux, A. Vignaud, P. Ciuciu.
Calibration-less parallel imaging compressed sensing reconstruction based on OSCAR regularization, September 2019, working paper or preprint.
https://hal.inria.fr/hal-02292372
[57]
D. Engemann, O. Kozynets, D. Sabbagh, G. Lemaître, G. Varoquaux, F. Liem, A. Gramfort.
Combining electrophysiology with MRI enhances learning of surrogate-biomarkers, December 2019, working paper or preprint. [ DOI : 10.1101/856336 ]
https://hal.archives-ouvertes.fr/hal-02395406
[58]
B. Hermann, F. Raimondo, L. Hirsch, Y. Huang, M. Valente, P. Pérez, D. Engemann, F. Faugeras, N. Weiss, S. Demeret, B. Rohaut, L. Parra, J. D. Sitt, L. Naccache.
Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness, April 2019, working paper or preprint. [ DOI : 10.1101/612309 ]
https://hal.archives-ouvertes.fr/hal-02107872
[59]
A. Jaiswal, J. Nenonen, M. Stenroos, A. Gramfort, S. S. Dalal, B. U. Westner, V. Litvak, J. C. Mosher, J.-M. Schoffelen, C. Witton, R. Oostenveld, L. Parkkonen.
Comparison of beamformer implementations for MEG source localization, November 2019, working paper or preprint. [ DOI : 10.1101/795799 ]
https://hal.inria.fr/hal-02369296
[60]
H. Janati, T. Bazeille, B. Thirion, M. Cuturi, A. Gramfort.
Multi-subject MEG/EEG source imaging with sparse multi-task regression, October 2019, working paper or preprint.
https://hal.inria.fr/hal-02304194
[61]
H. Janati, M. Cuturi, A. Gramfort.
Spatio-Temporal Alignments: Optimal transport through space and time, October 2019, working paper or preprint.
https://hal.inria.fr/hal-02309340
[62]
J. Josse, N. Prost, E. Scornet, G. Varoquaux.
On the consistency of supervised learning with missing values, March 2019, https://arxiv.org/abs/1902.06931 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02024202
[63]
D. La Rocca, P. Ciuciu, D. Engemann, V. Van Wassenhove.
Emergence of β and γ networks following multisensory training, February 2019, working paper or preprint. [ DOI : 10.1101/560235 ]
https://hal.archives-ouvertes.fr/hal-02052443
[64]
C. Lazarus, P. Weiss, L. E. Gueddari, F. Mauconduit, A. Vignaud, P. Ciuciu.
3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance imaging, March 2019, working paper or preprint.
https://hal.inria.fr/hal-02067080
[65]
A. Machlouzarides-Shalit, V. Iovene, N. Makris, D. Wassermann.
A Novel Sulcal Hierarchy Based on Manually Labelled Sulci, June 2019, Organization for Human Brain Mapping, Poster.
https://hal.inria.fr/hal-02347268
[66]
M. Massias, Q. Bertrand, A. Gramfort, J. Salmon.
Support recovery and sup-norm convergence rates for sparse pivotal estimation, January 2020, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02444978
[67]
M. Massias, S. Vaiter, A. Gramfort, J. Salmon.
Dual Extrapolation for Sparse Generalized Linear Models, August 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02263500
[68]
V. Menon, G. Guillermo, M. Pinsk, V.-D. Nguyen, J.-R. Li, W. Cai, D. Wassermann.
Quantitative modeling links in vivo microstructural and macrofunctional organization of human and macaque insular cortex, and predicts cognitive control abilities, January 2020, working paper or preprint. [ DOI : 10.1101/662601 ]
https://hal.inria.fr/hal-02434382
[69]
T. Moreau, A. Gramfort.
DiCoDiLe: Distributed Convolutional Dictionary Learning, November 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02371715
[70]
D. Sabbagh, P. Ablin, G. Varoquaux, A. Gramfort, D. Engemann.
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states, November 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02367411
[71]
M.-A. Schulz, B. T. Yeo, J. T. Vogelstein, J. Mourao-Miranada, J. N. Kather, K. Kording, B. A. Richards, D. Bzdok.
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets, September 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02276649
References in notes
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K. S. Button, J. P. Ioannidis, C. Mokrysz, B. A. Nosek, J. Flint, E. S. Robinson, M. R. Munafò.
Power failure: why small sample size undermines the reliability of neuroscience, in: Nature Reviews Neuroscience, 2013, vol. 14, no 5, pp. 365–376.
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Scanning the horizon: towards transparent and reproducible neuroimaging research, in: Nature Reviews Neuroscience, 2017, vol. 18, no 2, pp. 115–126.