1.6k citations · 11.3k across the 73 of their papers we have counts for
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On Using Backpropagation for Speech Texture Generation and Voice Conversion
Jan Chorowski, Ron J. Weiss, Rif A. Saurous +1
Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthes…
Time-Dependent Representation for Neural Event Sequence Prediction
Yang Li, Nan Du, Samy Bengio
Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between event…
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le +7
The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these req…
Tacotron: Towards End-to-End Speech Synthesis
Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton +11
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these component…
Revisiting Distributed Synchronous SGD
Xinghao Pan, Jianmin Chen, Rajat Monga +2
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the c…
Learning to Remember Rare Events
Łukasz Kaiser, Ofir Nachum, Aurko Roy +1
Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present…
N-gram Language Modeling using Recurrent Neural Network Estimation
Ciprian Chelba, Mohammad Norouzi, Samy Bengio
We investigate the effective memory depth of RNN models by using them for -gram language model (LM) smoothing. Experiments on a small corpus (UPenn Treebank, one million words o…
Sharp Minima Can Generalize For Deep Nets
Laurent Dinh, Razvan Pascanu, Samy Bengio +1
Despite their overwhelming capacity to overfit, deep learning architectures tend to generalize relatively well to unseen data, allowing them to be deployed in practice. However, ex…
Context-aware Captions from Context-agnostic Supervision
Ramakrishna Vedantam, Samy Bengio, Kevin Murphy +2
We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic…