Much of deep learning research offers incremental improvements on the state of the art methods. In this workshop we solicit papers that do not follow this narrow conception of science. In particular, we are interested in negative results that advance the understanding of deep learning through empirical falsification of a credible hypothesis.

Submissions should take care to make explicit the motivating principles behind the hypothesis being tested, and the implications of the results in relation to these motivating principles. We also encourage submissions that go a layer deeper and investigate the causes of the initial idea not working as expected. A good submission would allow the reader to positively answer the questions “Did I reliably learn something about neural networks that I didn’t know before?”

We invite submissions on the following topics:

• Examples of well-defined reasonable hypotheses that were later empirically falsified.

• Negative scientific findings in a more general sense, methodologies or tools that gave disappointing results, especially if lessons can be learned from these results in hindsight.

• Meta deep learning research - for example, discussion on the role that empirical investigation, mathematical proof, or general deductive should reasoning play in deep learning. As a field, do we value certain types of research over others?

• Intersections between machine learning research and philosophy of science in general

Technical submissions may center on deep-learning-adjacent fields (causal DL, meta-learning, generative modelling, adversarial examples, probabilistic reasoning, etc) or applications.

Selected papers will be optionally included in a special issue of PMLR. Alternatively, some authors may prefer their paper to be in the non-archival track which is to share preliminary findings that will later go to full review at another venue.

Formatting Instructions & Guidelines

For camera ready submissions plese use the workshop LaTeX style files.

Submissions should be 4-6 pages long (excluding references), and will be evaluated using the following criteria:

• Clarity of writing

• Rigor and transparency in the scientific methodologies employed

• Novelty and significance of insights

• Quality of discussion of limitations

• Reproducibility of results

Authors may include unlimited appendices but reviewers will not be required to take them into account. Where relevant, it is encouraged to include the checklist from the LaTeX template and a broader impact statement but these are neither required nor included in the page limit.

We welcome first time authors to submit to this workshop. The workshop will be run in-person with the possibility to attend virtually; papers may be submitted by both in-person and virtual attendees, although the latter will be limited to a 5 minute video posted on the workshop website.

Reviewers will nominate papers for spotlight and contributed talks, and two awards: the "Entropic Award," for most surprising negative result, and the "Didactic Award,"' for most well-explained and pedagogical paper. Reviewers will also nominate papers with exemplary scientific rigour and insightful findings for publication in a special edition of PMLR.

Papers should be submitted here.