NIPS*95 Workshop on Transfer in Inductive Systems

[Rich Caruana]

Post-NIPS*95 Workshop, December 1-2, 1995, Vail, Colorado

TITLE:   "Learning to Learn: Knowledge Consolidation 
             and Transfer in Inductive Systems"

ORGANIZERS:   Rich Caruana (co-chair), Danny Silver (co-chair),
              Jon Baxter, Tom Mitchell, Lori Pratt, Sebastian Thrun

INVITED TALKS BY:   Leo Breiman   (Berkeley)
                    Tom Mitchell  (CMU)
                    Tomaso Poggio (MIT)
                    Noel Sharkey  (Sheffield)
                    Jude Shavlik  (Wisconsin)

WEB PAGE: http://www.cs.cmu.edu/afs/cs/usr/caruana/pub/transfer.html

DESCRIPTION: 

Because the power of tabula rasa learning is limited, interest is
increasing in methods that capitalize on previously acquired domain
knowledge.  Examples of these methods include:

  o  using symbolic domain theories to bias connectionist networks
  o  using extra outputs on a connectionist network to bias the hidden
     layer representation towards more predictive features
  o  using unsupervised learning on a large corpus of unlabelled data
     to learn features useful for subsequent supervised learning on a
     smaller labelled corpus
  o  using models previously learned for other problems as a bias when 
     learning new, but related, problems

There are many approaches: hints, knowledge-based artificial neural
nets (KBANN), explanation-based neural nets (EBNN), multitask learning
(MTL), knowledge consolidation, ...  What they all have in common is
the attempt to transfer knowledge from other sources to benefit the
current inductive task.

The goal of this workshop is to provide an opportunity for researchers
and practitioners to discuss problems and progress in knowledge
transfer in learning.  We hope to identify research directions, debate
theories and approaches, discover unifying principles, and begin to
start answering questions like:

        o when will transfer help -- or hinder?
        o what should be transferred and how?
        o what are the benefits of transfer?
        o in what domains is transfer most useful?
        o is there evidence for transfer in nature?