We invite submissions for position and/or perspective talks that critically examine common practices and/or trends in theory, methodology, datasets, empirical or ethical standards, publication models, or any other aspect of deep learning research. While we are happy to bring attention to problems with no immediate solutions, we particularly encourage papers which propose a solution or indicate a way forward. Topics of interest include, but are not limited to:
- Theoretical understanding of deep learning
- Scalability challenges and solutions for deep learning
- Fairness, transparency, privacy, and ethical implications of deep learning
- Evaluating and debugging deep learning models
While we are interested in submissions that can be broadly characterized as position and/or perspective papers, we particularly welcome submissions that fall into one or more of the following categories.
Flawed Research Processes and Practices
We welcome submissions that highlight commonly encountered flawed practices at any stage of the research process. These can either be technical pitfalls pertaining to theoretical, methodological, empirical or ethical aspects of deep learning (See e.g., [1,2,3]), or more procedural practices (e.g, ). When possible, papers should also try to highlight work which does not fall pretty to flawed practices, as examples of how they can be avoided.
Unjustified Intuitions or Assumptions
We welcome submissions that call into question commonly held intuitions or provide clear evidence either for or against assumptions that are regularly taken for granted in the field of deep learning without proper justification. For example, we would like to see papers which provide empirical assessments to test out metrics, verify intuitions, or compare popular current approaches with historic baselines. Such submissions are encouraged regardless of whether these assessments ultimately result in positive or negative results. We would also like to see work which provides results which makes us rethink our intuitions or the assumptions we typically make in the field of deep learning.
We welcome submissions that show failure modes of theoretical, methodological, empirical or ethical aspects of deep learning or suggest new approaches which one might expect to perform well but which do not. The goal here is to provide a venue for such research on deep learning which is of interest to the broader community but which might otherwise go unpublished. We believe that such research could be incredibly useful in dissuading other researchers from pursuing similar ultimately unsuccessful approaches. Though submitted papers are typically expected to explain why the approach performs poorly, this is not essential if the paper is able to demonstrate why the negative result is of interest to the community in its own right.
We welcome submissions that describe either (a) unresolved questions in theoretical, methodological, empirical or ethical aspects of deep learning that need to be addressed (See e.g., ), (b) desirable operating characteristics for deep learning models in particular application areas that are yet to be achieved, or (c) new frontiers of deep learning research that require rethinking current practices.
Submission Instructions and Logistics
Each accepted submission will be presented as a talk (of duration 15 to 30 mins) at the deep learning day of the KDD 2021 conference which will be held virtually on August 18th, 2021.
Each submission should be a 2-page extended abstract formatted using the ACM paper template and uploaded as a PDF. The submission should include a detailed outline of all the material that the author(s) plan to discuss in their talk. The 2-page submission should also include a brief (1-2 paragraph) bio of the main speaker at the end. Authors are free to include an additional two pages of appendix/supplementary material in the submission pdf.
Submission Website: https://cmt3.research.microsoft.com/KDDDLD2021/Submission/Index
Paper Submission Deadline: July 1st, 2021 11.59pm AoE
Author Notification: July 14th, 2021 11.59pm AoE
Camera Ready Deadline: August 1st, 2021 11.59pm AoE
References: Marcus, Gary (2018). Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631.
 Rudin, Cynthia (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
 Lipton, Zachary C (2018). "The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery." Queue 16, no. 3: 31-57.
 Lipton, Zachary C., and Jacob Steinhardt (2018). "Troubling Trends in Machine Learning Scholarship." arXiv preprint arXiv:1807.03341.
 Amodei, Dario, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané (2016). "Concrete problems in AI safety." arXiv preprint arXiv:1606.06565.