We invite researchers and industry professionals to submit their papers on negative results, failed experiments, and unexpected challenges encountered in applying deep learning to real-world problems across industry and science. The primary goal of this workshop is to create a platform for open and honest discussion about the hurdles and roadblocks in applying deep learning. We believe that sharing these experiences is crucial for the advancement of the field, providing valuable insights that can prevent others from repeating the same mistakes and fostering a culture of transparency and learning. We invite submissions from novel, ongoing, and unpublished research that apply deep learning to various domains including, but not limited to, social sciences, biology, physics, chemistry, engineering, robotics, psychology, healthcare, neuroscience, marketing, economics, or finance. Submitted papers should contain the following four elements:

• A use case that was tackled with deep learning. 

• A solution for this type of use case was proposed in the deep learning literature

• A description of the (negative) outcome in the solution. 

• An investigation (and ideally an answer) to the question of why it did not work as promised by the deep learning literature. 

The potential reasons for failure may include but are not limited to data-related issues (e.g., distribution shift, bias, label quality, noisy measurement, quality of simulated data), model limitations (e.g., assumption violations, robustness, interpretability, scalability, representation misalignment), and deployment challenges (e.g., computational demands, hardware constraints). Besides these four points, papers will be assessed on:

• Rigor and transparency in the scientific methodologies employed. 

• Novelty and significance of insights.

• Quality of discussion of limitations.

• Reproducibility of results.

• Clarity of writing.

Accepted papers will be made available on Open Review and the workshop website. Selected papers with exemplary scientific rigor, insightful findings, and excellent presentation will be nominated by reviewers for optional inclusion in a special issue of PMLR. Authors of selected papers can opt out of formal publication in PMLR. Proceedings of our last workshop can be found here. Submissions that do not appear in the proceedings will be non-archival, and authors are free to submit them to other venues. 

Furthermore, the program chairs will nominate papers for the spotlight and contributed talks as well as two awards: (1) “Entropic Award” for the most surprising negative result and (2) “Didactic Award” for the most well-explained and pedagogical papers.

This year, ICLR is discontinuing the separate “Tiny Papers” track, and is instead requiring each workshop to accept short (2 pages in ICLR format) paper submissions, with an eye towards inclusion; see https://iclr.cc/Conferences/2025/CallForTinyPapers for more details. Authors of these papers will be earmarked for potential funding from ICLR, but need to submit a separate application for Financial Assistance that evaluates their eligibility. This application for Financial Assistance to attend ICLR 2025 will become available on https://iclr.cc/Conferences/2025/ at the beginning of February and close on March 2nd.

Formatting Instructions & Guidelines

• Submissions should be no more than 4 pages long (excluding references), and authors should consider the following:

  1. Authors may include unlimited appendices but reviewers will not be required to take them into account in their assessment of the submission.

  2. Where relevant, authors are encouraged to include the checklist from the LaTeX template and a broader impact statement, neither of which are included in the page limit.

  3. We welcome first-time authors to submit to this workshop. 

• The workshop will be run in person. 

• For the submissions please use the LaTeX style files that can be found here.

• We use OpenReview to host papers and submissions will be double-blind.

Additionally, we welcome contributions of tiny papers to our workshop. These are papers with the same structure and formatting instructions as seen in full workshop submissions, but with at most 2 pages of the main text. They are not required to contain all four elements mentioned above, but should at least highlight a problem in an application that was tackled with deep learning and a description of the (negative) outcome.

Important Dates:

• Papers Submission Deadline -  February 3rd, 2025 February 6th, 2025 (Open Review submission link can be found here)

• Notification of Acceptance/Rejection - March 5th, 2025 

• Camera-ready & poster submission - March 19th, 2025 (ICLR poster guidelines are available here)

• In-person workshop - April 28th, 2025

Updates will be posted on this website. For any questions, please reach out to us at cant.believe.it.is.not.better@gmail.com. 

We look forward to your submission!