25.8k citations · 55.9k across the 29 of their papers we have counts for
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Benchmarking Learning Efficiency in Deep Reservoir Computing
Hugo Cisneros, Josef Sivic, Tomas Mikolov
It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly…
CommAI: Evaluating the first steps towards a useful general AI
Marco Baroni, Armand Joulin, Allan Jabri +4
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses inst…
Alternative structures for character-level RNNs
Piotr Bojanowski, Armand Joulin, Tomas Mikolov
Recurrent neural networks are convenient and efficient models for language modeling. However, when applied on the level of characters instead of words, they suffer from several pro…
Zero-Shot Learning by Convex Combination of Semantic Embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio +5
Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image…
On the difficulty of training Recurrent Neural Networks
Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
There are two widely known issues with properly training Recurrent Neural Networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this pape…