NIPS 96 Workshop Announcement

[Jenny Orr]

NIPS 96 Workshop Announcement:
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                    Tricks of the Trade Workshop Schedule
                         Saturday December 6, 1996
                                Snowmass, CO
                          7:30am-10:30am, 4pm-7pm

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ORGANIZERS:

   Jenny Orr        Willamette University     gorr at willamette.edu
   Klaus Muller     GMD First,  Germany	      klaus at first.gmd.de
   Rich Caruana     Carnegie Mellon           caruana at cs.cmu.edu
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OBJECTIVES:

Using neural networks to solve difficult problems often requires as
much art as science.  Researchers and practitioners acquire, through
experience and word-of-mouth, techniques and heuristics that help them
succeed.  Often these ``tricks'' are theoretically well motivated.
Sometimes they're the result of trial and error.  In this workshop we
ask you to share the ``tricks'' you have found helpful. Our focus will
be mainly regression and classification.

For abstracts on talks and other information, see our web page at
        http://www.willamette.edu/~gorr/nipsws.htm
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                               Morning Session

7:30am: Jenny Orr, Welcome

7:35am: Yann LeCun, To be announced.

8:20am: Nicol Schraudolph, Bettering Backprop

8:35am: Martin Schlang, Stable on-line adaptation or initial learning

8:50am: 20 minute discussion and break

9:10am: Larry Yaeger, Reducing A Priori Biases Improves Recognition Accuracy
(at the Expense of Classification Accuracy)

9:50am: Steve Lawrence, Neural Network Classification and Unequal Prior
Class Probabilities

10:05am: Shumeet Baluja, Sampling Negative Instances by Collecting False
Positives

10:15am: Discussion and morning wrap-up

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                              Afternoon Session

4:00pm: Hans George Zimmermann, Training of a neural net in time series
analysis

4:40pm: Tony Plate, Convergence of hyperparameters in Mackay's Bayesian
Backpropagation

4:55pm: Jan Larsen, Design and Regularization of Neural Networks: The
Optimal Use of A Validation

5:10pm: Renee S. Renner, Optimization techniques for improving performance
and training of a constructive neural network

5:25pm: Patrick van der Smagt, Optimisation in feed-forward neural networks:
On conjugate gradient, network size, and local minima

5:40pm: 15 minute discussion and break

5:55pm: David Horn, Optimal Ensemble Averaging of Neural Networks

6:10pm: Timothy X Brown, Jump Connectivity

6:20pm: Chan Lai-Wan, Tricks that make recurrent networks work

6:30pm: Rich Caruana, 101 Fun (and Useful) Things to Do With Extra Outputs

6:45pm: Discussion and workshop wrap-up