Connectionists: [CFP]: ICLR 2021 Workshop on Learning to Learn

[Timothy Hospedales]

We invite submissions  of papers to the ICLR 2021 workshop on Learning to Learn. 

****** Key  Info: ******

Workshop Website:
https://sites.google.com/view/learning-2-learn/ <https://sites.google.com/view/learning-2-learn/>
The goal of  the workshop is to bring together scientists from different backgrounds to further the understanding and development of learning to learn algorithms.  In particular we are interested in creating a platform to enable the exchange between the fields of neuroscience  and machine learning

We welcome contributions on all aspects of Learning To Learn, and specifically encourage contributions with respect to: 

1. What do we know about how humans learn to learn? How much of this knowledge has already been realized in intelligent systems and what remains to be explored?
2. What should be our expectation of meta learning in intelligent systems? Are we currently evaluating the performance of meta learning in a meaningful way or are our learning problems and evaluations ill-posed? 
3. Related to 2), how does meta learning relate to the “no free lunch” theorem, and how do we keep this in mind when implementing learning to learn approaches in intelligent systems. 
4. How can we meta-learn in a lifelong learning setting? Should we meta-learn how to learn continuously or should we continuously meta-learn, or both? How do humans do it?


****** Key  Dates: ******
Submission  deadline: Feb 26th 2021
Workshop day:  May 8th 2021 (virtual)


****** Workshop Abstract******

Recent years  have seen a lot of interest in the use and development of learning-to-learn algorithms. In this workshop, we’d like to discuss how humans meta-learn, and what we can and should expect from learning-to-learn in the field of machine learning. In particular we are interested in creating a platform to enable the exchange between the fields of neuroscience and machine learning to create an opportunity to reflect upon questions that we believe are important to further advance the field of meta learning in the machine learning community. 

We believe  that it is an important moment for the machine learning community to reflect upon these questions in order to advance the field and increase its variety in approaching learning to learn. We hope that by fostering discussions between cognitive science and machine learning researchers, we enable both sides to draw inspiration to further the understanding and development of learning-to-learn algorithms.

Invited Speakers:
Ishita Dasgupta <https://ishita-dg.github.io/> (DeepMind)
Risto Miikkulainen <https://www.cs.utexas.edu/users/risto/> (University of Texas at Austin)
Edward  Grefenstette <https://www.egrefen.com/> (FAIR)
Jane Wang <http://www.janexwang.com/> (DeepMind)
Erin Grant (UC Berkeley)
Jennifer Raymon <https://profiles.stanford.edu/jennifer-raymond> (Stanford)


We look forward to receiving your submissions!

The organizing committee

Sarah Bechtle  (Max-Planck-Institute for Intelligent Systems, Tübingen)
Todor Davchev (University of Edinburgh)
Yevgen Chebotar  (Google Brain)
Timothy Hospedales  (University of Edinburgh and Samsung AI Research)
Franziska Meier  (Facebook AI Research)

Email: learningtolearn.iclr2021 at gmail.com <mailto:learningtolearn.iclr2021 at gmail.com>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20210122/338b4e53/attachment.html>
-------------- next part --------------
An embedded and charset-unspecified text was scrubbed...
Name: not available
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20210122/338b4e53/attachment.ksh>