14.5k citations · 90.1k across the 347 of their papers we have counts for
6 papers · 2 filters
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Ian J. Goodfellow, Mehdi Mirza, Da Xiao +2
Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "fo…
On the Challenges of Physical Implementations of RBMs
Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville +1
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in cl…
Pylearn2: a machine learning research library
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin +6
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been d…
Challenges in Representation Learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier +25
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the…
Maxout Networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza +2
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so n…
Joint Training Deep Boltzmann Machines for Classification
Ian J. Goodfellow, Aaron Courville, Yoshua Bengio
We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at…