524 lines
22 KiB
Text
524 lines
22 KiB
Text
#import "@preview/bloated-neurips:0.5.1": url
|
|
|
|
#let TODO = text(fill: red, [*[TODO]*])
|
|
#let answerNA = text(fill: gray, "[NA]")
|
|
#let answerNo = text(fill: red, "[NO]")
|
|
#let answerYes = text(fill: blue, "[YES]")
|
|
|
|
#pagebreak(weak: true)
|
|
|
|
#set heading(numbering: none)
|
|
|
|
= NeurIPS Paper Checklist
|
|
|
|
#let claim(
|
|
name: [],
|
|
question: [],
|
|
answer: [],
|
|
justification: [],
|
|
guidelines: [],
|
|
) = {
|
|
set list(indent: 1em, tight: false)
|
|
show list: set block(spacing: 10pt)
|
|
set par(spacing: 5.8pt)
|
|
[
|
|
*#name*
|
|
|
|
Question: #question
|
|
|
|
Answer: #answer
|
|
|
|
Justification: #justification
|
|
|
|
Guidelines:
|
|
|
|
#guidelines
|
|
]
|
|
}
|
|
|
|
+ #claim(
|
|
name: [Claims],
|
|
question: [
|
|
Do the main claims made in the abstract and introduction accurately reflect
|
|
the paper's contributions and scope?],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the abstract and introduction do not include the
|
|
claims made in the paper.
|
|
|
|
- The abstract and/or introduction should clearly state the claims made,
|
|
including the contributions made in the paper and important assumptions and
|
|
limitations. A No or NA answer to this question will not be perceived well
|
|
by the reviewers.
|
|
|
|
- The claims made should match theoretical and experimental results, and
|
|
reflect how much the results can be expected to generalize to other settings.
|
|
|
|
- It is fine to include aspirational goals as motivation as long as it is
|
|
clear that these goals are not attained by the paper.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Limitations],
|
|
question: [
|
|
Does the paper discuss the limitations of the work performed by the
|
|
authors?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper has no limitation while the answer No
|
|
means that the paper has limitations, but those are not discussed in the
|
|
paper.
|
|
|
|
- The authors are encouraged to create a separate "Limitations" section in
|
|
their paper.
|
|
|
|
- The paper should point out any strong assumptions and how robust the
|
|
results are to violations of these assumptions (e.g., independence
|
|
assumptions, noiseless settings, model well-specification, asymptotic
|
|
approximations only holding locally). The authors should reflect on how
|
|
these assumptions might be violated in practice and what the implications
|
|
would be.
|
|
|
|
- The authors should reflect on the scope of the claims made, e.g., if the
|
|
approach was only tested on a few datasets or with a few runs. In
|
|
general, empirical results often depend on implicit assumptions, which
|
|
should be articulated.
|
|
|
|
- The authors should reflect on the factors that influence the performance
|
|
of the approach. For example, a facial recognition algorithm may perform
|
|
poorly when image resolution is low or images are taken in low lighting.
|
|
Or a speech-to-text system might not be used reliably to provide closed
|
|
captions for online lectures because it fails to handle technical jargon.
|
|
|
|
- The authors should discuss the computational efficiency of the proposed
|
|
algorithms and how they scale with dataset size.
|
|
|
|
- If applicable, the authors should discuss possible limitations of their
|
|
approach to address problems of privacy and fairness.
|
|
|
|
- While the authors might fear that complete honesty about limitations
|
|
might be used by reviewers as grounds for rejection, a worse outcome
|
|
might be that reviewers discover limitations that aren't acknowledged in
|
|
the paper. The authors should use their best judgment and recognize that
|
|
individual actions in favor of transparency play an important role in
|
|
developing norms that preserve the integrity of the community. Reviewers
|
|
will be specifically instructed to not penalize honesty concerning
|
|
limitations.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Theory Assumptions and Proofs],
|
|
question: [
|
|
For each theoretical result, does the paper provide the full set of
|
|
assumptions and a complete (and correct) proof?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not include theoretical results.
|
|
|
|
- All the theorems, formulas, and proofs in the paper should be numbered
|
|
and cross-referenced.
|
|
|
|
- All assumptions should be clearly stated or referenced in the statement
|
|
of any theorems.
|
|
|
|
- The proofs can either appear in the main paper or the supplemental
|
|
material, but if they appear in the supplemental material, the authors
|
|
are encouraged to provide a short proof sketch to provide intuition.
|
|
|
|
- Inversely, any informal proof provided in the core of the paper should be
|
|
complemented by formal proofs provided in appendix or supplemental
|
|
material.
|
|
|
|
- Theorems and Lemmas that the proof relies upon should be properly
|
|
referenced.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Experimental Result Reproducibility],
|
|
question: [
|
|
Does the paper fully disclose all the information needed to reproduce the
|
|
main experimental results of the paper to the extent that it affects the
|
|
main claims and/or conclusions of the paper (regardless of whether the code
|
|
and data are provided or not)?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not include experiments.
|
|
|
|
- If the paper includes experiments, a No answer to this question will not
|
|
be perceived well by the reviewers: Making the paper reproducible is
|
|
important, regardless of whether the code and data are provided or not.
|
|
|
|
- If the contribution is a dataset and/or model, the authors should
|
|
describe the steps taken to make their results reproducible or verifiable.
|
|
|
|
- Depending on the contribution, reproducibility can be accomplished in
|
|
various ways. For example, if the contribution is a novel architecture,
|
|
describing the architecture fully might suffice, or if the contribution
|
|
is a specific model and empirical evaluation, it may be necessary to
|
|
either make it possible for others to replicate the model with the same
|
|
dataset, or provide access to the model. In general. releasing code and
|
|
data is often one good way to accomplish this, but reproducibility can
|
|
also be provided via detailed instructions for how to replicate the
|
|
results, access to a hosted model (e.g., in the case of a large language
|
|
model), releasing of a model checkpoint, or other means that are
|
|
appropriate to the research performed.
|
|
|
|
- While NeurIPS does not require releasing code, the conference does
|
|
require all submissions to provide some reasonable avenue for
|
|
reproducibility, which may depend on the nature of the contribution. For
|
|
example
|
|
|
|
#set enum(numbering: "(a)")
|
|
|
|
+ If the contribution is primarily a new algorithm, the paper should make
|
|
it clear how to reproduce that algorithm.
|
|
+ If the contribution is primarily a new model architecture, the paper
|
|
should describe the architecture clearly and fully.
|
|
+ If the contribution is a new model (e.g., a large language model), then
|
|
there should either be a way to access this model for reproducing the
|
|
results or a way to reproduce the model (e.g., with an open-source
|
|
dataset or instructions for how to construct the dataset).
|
|
+ We recognize that reproducibility may be tricky in some cases, in which
|
|
case authors are welcome to describe the particular way they provide
|
|
for reproducibility. In the case of closed-source models, it may be
|
|
that access to the model is limited in some way (e.g., to registered
|
|
users), but it should be possible for other researchers to have some
|
|
path to reproducing or verifying the results.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Open access to data and code],
|
|
question: [
|
|
Does the paper provide open access to the data and code, with sufficient
|
|
instructions to faithfully reproduce the main experimental results, as
|
|
described in supplemental material?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that paper does not include experiments requiring
|
|
code.
|
|
|
|
- Please see the NeurIPS code and data submission guidelines
|
|
(#url("https://nips.cc/public/guides/CodeSubmissionPolicy")) for more
|
|
details.
|
|
|
|
- While we encourage the release of code and data, we understand that this
|
|
might not be possible, so "No" is an acceptable answer. Papers cannot be
|
|
rejected simply for not including code, unless this is central to the
|
|
contribution (e.g., for a new open-source benchmark).
|
|
|
|
- The instructions should contain the exact command and environment needed
|
|
to run to reproduce the results. See the NeurIPS code and data submission
|
|
guidelines (#url("https://nips.cc/public/guides/CodeSubmissionPolicy"))
|
|
for more details.
|
|
|
|
- The authors should provide instructions on data access and preparation,
|
|
including how to access the raw data, preprocessed data, intermediate
|
|
data, and generated data, etc.
|
|
|
|
- The authors should provide scripts to reproduce all experimental results
|
|
for the new proposed method and baselines. If only a subset of
|
|
experiments are reproducible, they should state which ones are omitted
|
|
from the script and why.
|
|
|
|
- At submission time, to preserve anonymity, the authors should release
|
|
anonymized versions (if applicable).
|
|
|
|
- Providing as much information as possible in supplemental material
|
|
(appended to the paper) is recommended, but including URLs to data and
|
|
code is permitted.
|
|
])
|
|
|
|
|
|
+ #claim(
|
|
name: [Experimental Setting/Details],
|
|
question: [
|
|
Does the paper specify all the training and test details (e.g., data
|
|
splits, hyperparameters, how they were chosen, type of optimizer, etc.)
|
|
necessary to understand the results?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not include experiments.
|
|
|
|
- The experimental setting should be presented in the core of the paper to
|
|
a level of detail that is necessary to appreciate the results and make
|
|
sense of them.
|
|
|
|
- The full details can be provided either with the code, in appendix, or as
|
|
supplemental material.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Experiment Statistical Significance],
|
|
question: [
|
|
Does the paper report error bars suitably and correctly defined or other
|
|
appropriate information about the statistical significance of the
|
|
experiments?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not include experiments.
|
|
|
|
- The authors should answer "Yes" if the results are accompanied by error
|
|
bars, confidence intervals, or statistical significance tests, at least
|
|
for the experiments that support the main claims of the paper.
|
|
|
|
- The factors of variability that the error bars are capturing should be
|
|
clearly stated (for example, train/test split, initialization, random
|
|
drawing of some parameter, or overall run with given experimental
|
|
conditions).
|
|
|
|
- The method for calculating the error bars should be explained (closed
|
|
form formula, call to a library function, bootstrap, etc.)
|
|
|
|
- The assumptions made should be given (e.g., Normally distributed errors).
|
|
|
|
- It should be clear whether the error bar is the standard deviation or the
|
|
standard error of the mean.
|
|
|
|
- It is OK to report 1-sigma error bars, but one should state it. The
|
|
authors should preferably report a 2-sigma error bar than state that they
|
|
have a 96% CI, if the hypothesis of Normality of errors is not verified.
|
|
|
|
- For asymmetric distributions, the authors should be careful not to show
|
|
in tables or figures symmetric error bars that would yield results that
|
|
are out of range (e.g. negative error rates).
|
|
|
|
- If error bars are reported in tables or plots, The authors should explain
|
|
in the text how they were calculated and reference the corresponding
|
|
figures or tables in the text.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Experiments Compute Resources],
|
|
question: [
|
|
For each experiment, does the paper provide sufficient information on the
|
|
computer resources (type of compute workers, memory, time of execution)
|
|
needed to reproduce the experiments?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not include experiments.
|
|
|
|
- The paper should indicate the type of compute workers CPU or GPU,
|
|
internal cluster, or cloud provider, including relevant memory and storage.
|
|
|
|
- The paper should provide the amount of compute required for each of the
|
|
individual experimental runs as well as estimate the total compute.
|
|
|
|
- The paper should disclose whether the full research project required more
|
|
compute than the experiments reported in the paper (e.g., preliminary or
|
|
failed experiments that didn't make it into the paper).
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Code Of Ethics],
|
|
question: [
|
|
Does the research conducted in the paper conform, in every respect, with
|
|
the NeurIPS Code of Ethics
|
|
#url("https://neurips.cc/public/EthicsGuidelines")
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the authors have not reviewed the NeurIPS Code
|
|
of Ethics.
|
|
|
|
- If the authors answer No, they should explain the special circumstances
|
|
that require a deviation from the Code of Ethics.
|
|
|
|
- The authors should make sure to preserve anonymity (e.g., if there is a
|
|
special consideration due to laws or regulations in their jurisdiction).
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Broader Impacts],
|
|
question: [
|
|
Does the paper discuss both potential positive societal impacts and
|
|
negative societal impacts of the work performed?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that there is no societal impact of the work
|
|
performed.
|
|
|
|
- If the authors answer NA or No, they should explain why their work has no
|
|
societal impact or why the paper does not address societal impact.
|
|
|
|
- Examples of negative societal impacts include potential malicious or
|
|
unintended uses (e.g., disinformation, generating fake profiles,
|
|
surveillance), fairness considerations (e.g., deployment of technologies
|
|
that could make decisions that unfairly impact specific groups), privacy
|
|
considerations, and security considerations.
|
|
|
|
- The conference expects that many papers will be foundational research and
|
|
not tied to particular applications, let alone deployments. However, if
|
|
there is a direct path to any negative applications, the authors should
|
|
point it out. For example, it is legitimate to point out that an
|
|
improvement in the quality of generative models could be used to generate
|
|
deepfakes for disinformation. On the other hand, it is not needed to
|
|
point out that a generic algorithm for optimizing neural networks could
|
|
enable people to train models that generate Deepfakes faster.
|
|
|
|
- The authors should consider possible harms that could arise when the
|
|
technology is being used as intended and functioning correctly, harms
|
|
that could arise when the technology is being used as intended but gives
|
|
incorrect results, and harms following from (intentional or
|
|
unintentional) misuse of the technology.
|
|
|
|
- If there are negative societal impacts, the authors could also discuss
|
|
possible mitigation strategies (e.g., gated release of models, providing
|
|
defenses in addition to attacks, mechanisms for monitoring misuse,
|
|
mechanisms to monitor how a system learns from feedback over time,
|
|
improving the efficiency and accessibility of ML).
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Safeguards],
|
|
question: [
|
|
Does the paper describe safeguards that have been put in place for
|
|
responsible release of data or models that have a high risk for misuse
|
|
(e.g., pretrained language models, image generators, or scraped datasets)?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper poses no such risks.
|
|
|
|
- Released models that have a high risk for misuse or dual-use should be
|
|
released with necessary safeguards to allow for controlled use of the
|
|
model, for example by requiring that users adhere to usage guidelines or
|
|
restrictions to access the model or implementing safety filters.
|
|
|
|
- Datasets that have been scraped from the Internet could pose safety
|
|
risks. The authors should describe how they avoided releasing unsafe
|
|
images.
|
|
|
|
- We recognize that providing effective safeguards is challenging, and many
|
|
papers do not require this, but we encourage authors to take this into
|
|
account and make a best faith effort.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Licenses for existing assets],
|
|
question: [
|
|
Are the creators or original owners of assets (e.g., code, data, models),
|
|
used in the paper, properly credited and are the license and terms of use
|
|
explicitly mentioned and properly respected?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not use existing assets.
|
|
|
|
- The authors should cite the original paper that produced the code package
|
|
or dataset.
|
|
|
|
- The authors should state which version of the asset is used and, if
|
|
possible, include a URL.
|
|
|
|
- The name of the license (e.g., CC-BY 4.0) should be included for each
|
|
asset.
|
|
|
|
- For scraped data from a particular source (e.g., website), the copyright
|
|
and terms of service of that source should be provided.
|
|
|
|
- If assets are released, the license, copyright information, and terms of
|
|
use in the package should be provided. For popular datasets,
|
|
#url("https://paperswithcode.com/datasets") has curated licenses for some
|
|
datasets. Their licensing guide can help determine the license of a
|
|
dataset.
|
|
|
|
- For existing datasets that are re-packaged, both the original license and
|
|
the license of the derived asset (if it has changed) should be provided.
|
|
|
|
- If this information is not available online, the authors are encouraged
|
|
to reach out to the asset's creators.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [New Assets],
|
|
question: [
|
|
Are new assets introduced in the paper well documented and is the
|
|
documentation provided alongside the assets?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not release new assets.
|
|
|
|
- Researchers should communicate the details of the dataset/code/model as
|
|
part of their submissions via structured templates. This includes details
|
|
about training, license, limitations, etc.
|
|
|
|
- The paper should discuss whether and how consent was obtained from people
|
|
whose asset is used.
|
|
|
|
- At submission time, remember to anonymize your assets (if applicable).
|
|
You can either create an anonymized URL or include an anonymized zip file.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [Crowdsourcing and Research with Human Subjects],
|
|
question: [
|
|
For crowdsourcing experiments and research with human subjects, does the
|
|
paper include the full text of instructions given to participants and
|
|
screenshots, if applicable, as well as details about compensation (if any)?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not involve crowdsourcing nor
|
|
research with human subjects.
|
|
|
|
- Including this information in the supplemental material is fine, but if
|
|
the main contribution of the paper involves human subjects, then as much
|
|
detail as possible should be included in the main paper.
|
|
|
|
- According to the NeurIPS Code of Ethics, workers involved in data
|
|
collection, curation, or other labor should be paid at least the minimum
|
|
wage in the country of the data collector.
|
|
])
|
|
|
|
+ #claim(
|
|
name: [
|
|
Institutional Review Board (IRB) Approvals or Equivalent for Research with
|
|
Human Subjects
|
|
],
|
|
question: [
|
|
Does the paper describe potential risks incurred by study participants,
|
|
whether such risks were disclosed to the subjects, and whether
|
|
Institutional Review Board (IRB) approvals (or an equivalent
|
|
approval/review based on the requirements of your country or institution)
|
|
were obtained?
|
|
],
|
|
answer: TODO, // Replace by answerYes, answerNo, or answerNA.
|
|
justification: TODO,
|
|
guidelines: [
|
|
- The answer NA means that the paper does not involve crowdsourcing nor
|
|
research with human subjects.
|
|
|
|
- Depending on the country in which research is conducted, IRB approval (or
|
|
equivalent) may be required for any human subjects research. If you
|
|
obtained IRB approval, you should clearly state this in the paper.
|
|
|
|
- We recognize that the procedures for this may vary significantly between
|
|
institutions and locations, and we expect authors to adhere to the
|
|
NeurIPS Code of Ethics and the guidelines for their institution.
|
|
|
|
- For initial submissions, do not include any information that would break
|
|
anonymity (if applicable), such as the institution conducting the review.
|
|
])
|