Talks and Poster Presentations (with Proceedings-Entry):

E. Merdivan, M. Loghmani, M. Geist:
"Reconstruct & Crush Networks";
Talk: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA; 12-04-2017 - 12-09-2017; in: "NIPS 2017", (2017), 9 pages.

English abstract:
This article introduces an energy-based model that is adversarial regarding data:
it minimizes the energy for a given data distribution (the positive samples) while
maximizing the energy for another given data distribution (the negative or unlabeled
samples). The model is especially instantiated with autoencoders where the energy,
represented by the reconstruction error, provides a general distance measure for
unknown data. The resulting neural network thus learns to reconstruct data from the
first distribution while crushing data from the second distribution. This solution can
handle different problems such as Positive and Unlabeled (PU) learning or covariate
shift, especially with imbalanced data. Using autoencoders allows handling a large
variety of data, such as images, text or even dialogues. Our experiments show
the flexibility of the proposed approach in dealing with different types of data in
different settings: images with CIFAR-10 and CIFAR-100 (not-in-training setting),
text with Amazon reviews (PU learning) and dialogues with Facebook bAbI (next
response classification and dialogue completion).

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