Science and Engineering of Deep Learning

We aim to create a venue where we discuss seemingly contrasting challenges in machine learning research and their consequences. We invite researchers to discuss the boundaries between science and engineering, the implications of having blurred boundaries, and their potential consequences in areas of life beyond research.

We organized the first ``Science meets Engineering in Deep Learning'' workshop at NeurIPS 2019, which aimed to identify the potential boundaries between science and engineering and the role of theoretically driven and application-driven research in deep learning. The workshop's discussions highlighted how intertwined science and engineering are and emphasized the benefits of their symbiotic relationship to push the boundaries of both theoretically driven and application-driven research. To highlight the communication channel we aimed to build, we chose "Science meets Engineering'' in the title for the first iteration of the workshop.

Since then, such boundaries appear harder and harder to draw, and it becomes increasingly clear that we need to agree on a set of values that define us as a community, and that will shape our future research. In particular, we envision that such values will help (1) emphasize important engineering and scientific practices that we should foster to increase the robustness of our research, (2) acknowledge the broader impact of our research, and (3) abide by ethical standards.

Reflecting this shift in perspective, this year's proposed title is "Science and Engineering of Deep Learning''. With this in mind, we are proposing the second iteration of the workshop for ICLR 2021, focusing on the core themes mentioned above. In particular, we would like to ask (1) "What are the scientific and engineering practices that we should promote as a community?" and "How do those interact?", and (2) "What is the broader impact of such adopted scientific and engineering practices?"

https://sites.google.com/view/sedl-workshop