10.8k citations · 17.5k across the 28 of their papers we have counts for
7 papers · 1 filter
Mollifying Networks
Caglar Gulcehre, Marcin Moczulski, Francesco Visin +1
The optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non-convex nature of the loss function, e.g. it can…
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho +1
We extend neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for e…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil +1
Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies…
Pointing the Unknown Words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati +2
The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the d…
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre +4
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scal…
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos +2
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two di…