posted by organizer: davidjrohde || 1505 views || tracked by 4 users: [display]

ICBINB 2022 : I Can't Believe It Is Not Better: Understanding Deep Learning Through Empirical Falsification

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/icbinb-2022/submit
 
When Dec 3, 2022 - Dec 3, 2022
Where New Orleans
Submission Deadline Sep 22, 2022
Notification Due Oct 20, 2022
Final Version Due Oct 20, 2022
Categories    machine learning   deep learning   philosophy of science
 

Call For Papers

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.

Related Resources

I-DO 2024   The 2024 International Conference on Information Technology, Data Science and Optimization
ACM-Ei/Scopus-CCISS 2024   2024 International Conference on Computing, Information Science and System (CCISS 2024)
i-Society 2024   The 17th International Conference on Information Society
ECAI 2024   27th European Conference on Artificial Intelligence
CAiSE 2024   36th International Conference on Advanced Information Systems Engineering
ICDM 2024   IEEE International Conference on Data Mining
HCIS-IS 2024   IS02: Edge Computing Technologies for Mobile Computing and Internet of Things (4th Edition)
AIM@EPIA 2024   Artificial Intelligence in Medicine
IADIS IS 2024   17th IADIS International Conference Information Systems 2024
ICMLA 2024   23rd International Conference on Machine Learning and Applications