1.6k citations · 4.2k across the 75 of their papers we have counts for
7 papers · 1 filter
Adversarial Autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly +2
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform…
A Neural Transducer
Navdeep Jaitly, David Sussillo, Quoc V. Le +3
Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data a…
Object Recognition from Short Videos for Robotic Perception
Ivan Bogun, Anelia Angelova, Navdeep Jaitly
Deep neural networks have become the primary learning technique for object recognition. Videos, unlike still images, are temporally coherent which makes the application of deep net…
Listen, Attend and Spell
William Chan, Navdeep Jaitly, Quoc V. Le +1
We present Listen, Attend and Spell (LAS), a neural network that learns to transcribe speech utterances to characters. Unlike traditional DNN-HMM models, this model learns all the…
Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequ…
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly +1
Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current…
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated o…