Title: No title found

Abstract: should be indented 1/2 inch (3 picas) on both left and righthand margins. Use 10 point type, with a vertical spacing of 11 points.

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The file iclr2020_conference.pdf contains these instructions and illustrates the various formatting requirements your ICLR paper must satisfy. Submissions must be made using L A T E X and the style files iclr2020_conference.sty and iclr2020_conference.bst (to be used with L A T E X2e). The file iclr2020_conference.tex may be used as a "shell" for writing your paper. All you have to do is replace the author, title, abstract, and text of the paper with your own. The formatting instructions contained in these style files are summarized in sections 2, 3, and 4 below.

2. GENERAL FORMATTING INSTRUCTIONS:

The text must be confined within a rectangle 5.5 inches (33 picas) wide and 9 inches (54 picas) long. The left margin is 1.5 inch (9 picas). Use 10 point type with a vertical spacing of 11 points. Times New Roman is the preferred typeface throughout. Paragraphs are separated by 1/2 line space, with no indentation.

Paper title is 17 point, in small caps and left-aligned. All pages should start at 1 inch (6 picas) from the top of the page. Authors' names are set in boldface, and each name is placed above its corresponding address. The lead author's name is to be listed first, and the co-authors' names are set to follow. Authors sharing the same address can be on the same line.

Please pay special attention to the instructions in section 4 regarding figures, tables, acknowledgments, and references.

The recommended paper length is 8 pages, with unlimited additional pages for citations. There will be a strict upper limit of 10 pages for the main text. Reviewers will be instructed to apply a higher standard to papers in excess of 8 pages. Authors may use as many pages of appendices (after the bibliography) as they wish, but reviewers are not required to read these.

3. HEADINGS: FIRST LEVEL:

First level headings are in small caps, flush left and in point size 12. One line space before the first level heading and 1/2 line space after the first level heading.

3.1. HEADINGS: SECOND LEVEL:

Second level headings are in small caps, flush left and in point size 10. One line space before the second level heading and 1/2 line space after the second level heading.

3.1.1. HEADINGS: THIRD LEVEL:

Third level headings are in small caps, flush left and in point size 10. One line space before the third level heading and 1/2 line space after the third level heading.

4. CITATIONS, FIGURES, TABLES, REFERENCES:

These instructions apply to everyone, regardless of the formatter being used.

4.1. CITATIONS WITHIN THE TEXT:

Citations within the text should be based on the natbib package and include the authors' last names and year (with the "et al." construct for more than two authors). When the authors or the publication are included in the sentence, the citation should not be in parenthesis using \citet{} (as in "See Hinton et al. (2006) for more information."). Otherwise, the citation should be in parenthesis using \citep{} (as in "Deep learning shows promise to make progress towards AI (Bengio & LeCun, 2007) .").

The corresponding references are to be listed in alphabetical order of authors, in the REFERENCES section. As to the format of the references themselves, any style is acceptable as long as it is used consistently.

4.2. FOOTNOTES:

Indicate footnotes with a number1 in the text. Place the footnotes at the bottom of the page on which they appear. Precede the footnote with a horizontal rule of 2 inches (12 picas). 

matrix J ∈ R m×n of f : R n → R m ∇ 2 x f (x) or H(f )(x)

The Hessian matrix of f at input point x f (x)dx Definite integral over the entire domain of x S f (x)dx Definite integral with respect to x over the set S Probability and Information Theory

P (a):

A probability distribution over a discrete variable p(a)

A probability distribution over a continuous variable, or over a variable whose type has not been specified a ∼ P Random variable a has distribution P A number of width problems arise when LaTeX cannot properly hyphenate a line. Please give LaTeX hyphenation hints using the \-command.

E x∼P [f (x)] or Ef (x) Expectation of f (x) with respect to P (x) Var(f (x)) Variance of f (x) under P (x) Cov(f (x), g(x)) Covariance of f (x)

AUTHOR CONTRIBUTIONS:

If you'd like to, you may include a section for author contributions as is done in many journals. This is optional and at the discretion of the authors.

Sample of the first footnote

Sample of the second footnote

Figures and Tables:

2

All artwork must be neat, clean, and legible. Lines should be dark enough for purposes of reproduction; art work should not be hand-drawn. The figure number and caption always appear after the figure. Place one line space before the figure caption, and one line space after the figure. The figure caption is lower case (except for first word and proper nouns); figures are numbered consecutively.

Sample table title Make sure the figure caption does not get separated from the figure. Leave sufficient space to avoid splitting the figure and figure caption. You may use color figures. However, it is best for the figure captions and the paper body to make sense if the paper is printed either in black/white or in color. All tables must be centered, neat, clean and legible. Do not use hand-drawn tables. The table number and title always appear before the table. See Table 1. Place one line space before the table title, one line space after the table title, and one line space after the table. The table title must be lower case (except for first word and proper nouns); tables are numbered consecutively.

Table content: <table><tr><td>PART</td><td>DESCRIPTION</td></tr><tr><td colspan="2">Dendrite Input terminal</td></tr><tr><td>Axon</td><td>Output terminal</td></tr><tr><td>Soma</td><td>Cell body (contains cell nucleus)</td></tr></table>

Do not change any aspects of the formatting parameters in the style files. In particular, do not modify the width or length of the rectangle the text should fit into, and do not change font sizes (except perhaps in the REFERENCES section; see below). Please note that pages should be numbered.Most of the margin problems come from figures positioned by hand using \special or other commands. We suggest using the command \includegraphics from the graphicx package. Always specify the figure width as a multiple of the line width as in the example below using .eps graphics \usepackage[dvips]{graphicx} ... \includegraphics[width=0.8\linewidth]{myfile.eps} or \usepackage[pdftex]{graphicx} ... \includegraphics[width=0.8\linewidth]{myfile.pdf} for .pdf graphics. See section 4.4 in the graphics bundle documentation (http://www.ctan. org/tex-archive/macros/latex/required/graphics/grfguide.ps)

Table content: <table><tr><td colspan="2">6 FINAL INSTRUCTIONS</td><td/></tr><tr><td colspan="3">7 PREPARING POSTSCRIPT OR PDF FILES</td></tr><tr><td colspan="3">Please prepare PostScript or PDF files with paper size "US Letter", and not, for example, "A4". The</td></tr><tr><td colspan="3">-t letter option on dvips will produce US Letter files.</td></tr><tr><td colspan="3">Consider directly generating PDF files using pdflatex (especially if you are a MiKTeX user).</td></tr><tr><td colspan="3">PDF figures must be substituted for EPS figures, however.</td></tr><tr><td colspan="3">Otherwise, please generate your PostScript and PDF files with the following commands:</td></tr><tr><td colspan="3">dvips mypaper.dvi -t letter -Ppdf -G0 -o mypaper.ps</td></tr><tr><td colspan="2">ps2pdf mypaper.ps mypaper.pdf</td><td/></tr><tr><td colspan="2">7.1 MARGINS IN LATEX</td><td/></tr><tr><td>H(x)</td><td colspan="2">Shannon entropy of the random variable x</td></tr><tr><td>D KL (P Q)</td><td colspan="2">Kullback-Leibler divergence of P and Q</td></tr><tr><td>N (x; µ, Σ)</td><td colspan="2">Gaussian distribution over x with mean µ and covariance</td></tr><tr><td/><td>Σ</td><td/></tr><tr><td/><td colspan="2">Functions</td></tr><tr><td>f : A → B</td><td colspan="2">The function f with domain A and range B</td></tr><tr><td>f • g</td><td colspan="2">Composition of the functions f and g</td></tr><tr><td>f (x; θ)</td><td colspan="2">A function of x parametrized by θ. (Sometimes we write</td></tr><tr><td/><td colspan="2">f (x) and omit the argument θ to lighten notation)</td></tr><tr><td>log x</td><td colspan="2">Natural logarithm of x</td></tr><tr><td>σ(x)</td><td>Logistic sigmoid,</td><td>1 1 + exp(-x)</td></tr><tr><td>ζ(x)</td><td colspan="2">Softplus, log(1 + exp(x))</td></tr><tr><td>||x|| p</td><td>L p norm of x</td><td/></tr><tr><td>||x||</td><td>L 2 norm of x</td><td/></tr><tr><td>x +</td><td colspan="2">Positive part of x, i.e., max(0, x)</td></tr><tr><td>1 condition</td><td colspan="2">is 1 if the condition is true, 0 otherwise</td></tr></table>

Peer review (iclr2020):
EEG-based brain-computer interfaces is a difficult task, as the signal is noisy, vary from subject to subject and classical machine learning techniques are still deficient for certain subjects. The authors propose a DWT-based deep net, where convolutional layers are applied on a wavelet decomposition of the signal. To avoid the curse of dimensionality, only half of the wavelet coefficient are used to train the convnet. A higher dropout is applied to avoid overfit. The proposed net is evaluated on a dataset and different wavelet transforms are assessed. This contribution is not convincing enough. The idea to use DWT is interesting but not new and the results are only tested on one small dataset. The proposed approach should be applied on different types of BCI task (P300, error potentials, etc), as it is the case in Lawhern et al. (2018) or at least on several datasets of motor imagery, see [1] for example of available datasets. The results should be compared to state-of-the-art techniques, that is tangent space classification using Riemannian geometry, see [2]. Comparing the results with FBCSP is not enough. To improve this contribution, the authors could make the following modifications. The experimental protocol section is not really relevant, as it is already described in the literature and the authors use the data in a common way. The last paragraph of this section is interesting, the rest could be dropped. The Proposed method section could include a figure to show the sequence of preprocessing applied to signal and the shape of the deep net. The results part is a bit hard to read as it just a description of the table. A figure with star indicating significant statistical difference could help to ease the reading. [1] Jayaram, V., & Barachant, A. (2018). MOABB: trustworthy algorithm benchmarking for BCIs. Journal of neural engineering, 15(6), 066011. [2] Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of neural engineering, 15(3), 031005. p1: (the decease) -> decrease