Yann Dauphin

Publications51
h-index32
Citations16,255
Highly Influential Citations2,141
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mixup: Beyond Empirical Risk Minimization
TLDR
This work proposes mixup, a simple learning principle that trains a neural network on convex combinations of pairs of examples and their labels, which improves the generalization of state-of-the-art neural network architectures.
View PDF on arXiv
Convolutional Sequence to Sequence Learning
TLDR
This work introduces an architecture based entirely on convolutional neural networks, which outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT-French translation at an order of magnitude faster speed, both on GPU and CPU.
View PDF on arXiv
Language Modeling with Gated Convolutional Networks
TLDR
A finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens, is developed and is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.
View PDF on arXiv
Theano: A Python framework for fast computation of mathematical expressions
TLDR
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
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Hierarchical Neural Story Generation
TLDR
This work collects a large dataset of 300K human-written stories paired with writing prompts from an online forum that enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.
View on ACL
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
TLDR
This paper proposes a new approach to second-order optimization, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods, and applies this algorithm to deep or recurrent neural network training, and provides numerical evidence for its superior optimization performance.
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Parseval Networks: Improving Robustness to Adversarial Examples
TLDR
It is shown that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers while being more robust than their vanilla counterpart against adversarial examples.
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Pay Less Attention with Lightweight and Dynamic Convolutions
TLDR
It is shown that a very lightweight convolution can perform competitively to the best reported self-attention results, and dynamic convolutions are introduced which are simpler and more efficient than self-ATTention.
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Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
TLDR
This paper implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants, and implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark.
View on ACM
Deal or No Deal? End-to-End Learning of Negotiation Dialogues
TLDR
For the first time, it is shown it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states, and this technique dramatically improves performance.
View on ACL
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