The Dresden Deep Learning Hackathon ( #d3hack2019 ) is meant to bring together machine learning experts and scientific practitioners. Teams of 2-4 scientists can apply for the hackathon given a scientific problem they want to solve with machine learning. Upon approval, they will be assisted by one or two machine learning experts for 5 days consecutively! This effort is meant to give your team a head-start and potentially create an end-to-end machine learning solution for your science. The teams are motivated to publish a scientific paper about the hackathon efforts at dedicated conferences or in established journals - at best jointly with their mentors - after the hackathon. A win-win situation for all parties involved.
The scope of scientific domains that can apply is not limited. For sure, our mentors have a given background mostly with regard to 2D or 3D images. So we will try to match that as close as possible. However, we are still in the process of fixing mentors (we have expressions of interest of about 5 more than listed below). We will also consider a limited amount of applications using standard machine learning (MLP, SVM, RandomForests,...).
If you are unclear whether your topic fits the hackathon, please reach out to us.
Most importantly, any team without a readily available data set for training will be discarded from the candidate list. In other words, if you are interested in applying machine learning to your data, you shouldn't use the hackathon to annotate your data.
The workshop admission fee amounts to € 300 per participant to cover room rent and catering. We are still looking for sponsors, so there is a non-negligible probability that the admission fee will be reduced in the future.
The call for applications closes on June 30, 2019, at 12pm AoE! We especially invite women and under-represented minorities to apply! After the aforementioned date, a review board of mentors and organizers will judge the applications and send out confirmations to the applying teams until mid July the latest. The registration mechanism of participants will be circulated then. Applications by members of any tier of the academic profession hierarchy (student, TA, apprentice, PhD candidate/predoc, post-doc, PI, specialist, RSE, ...).
For members of non-academic institutions: We cannot allow applications from non-academic institutions or industry to our hackathon. If you want to participate with a project as a company, this project needs to be embedded in a scientific group and the majority of team members need to be employed by a scientific institution. On top, the results of the hackathon are expected to be published. So be prepared to undisclose your results and (if possible) the data and code which produced these results.
During the workshop, we offer computational powerful compute resources at the TU Dresden as well as in the AWS cloud. Participants are henceforth only required to bring their laptops. If you have security concerns regarding your data, you can also apply and work on your hardware at home while being mentored in Dresden. Contact us for details and recipes.