7 research outputs found
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on attention mechanisms, dispensing with recurrence and convolutions
entirely. Experiments on two machine translation tasks show these models to be
superior in quality while being more parallelizable and requiring significantly
less time to train. Our model achieves 28.4 BLEU on the WMT 2014
English-to-German translation task, improving over the existing best results,
including ensembles by over 2 BLEU. On the WMT 2014 English-to-French
translation task, our model establishes a new single-model state-of-the-art
BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction
of the training costs of the best models from the literature. We show that the
Transformer generalizes well to other tasks by applying it successfully to
English constituency parsing both with large and limited training data.Comment: 15 pages, 5 figure
Fast Neural Network Predictions from Constrained Aerodynamics Datasets
Incorporating computational fluid dynamics in the design process of jets,
spacecraft, or gas turbine engines is often challenged by the required
computational resources and simulation time, which depend on the chosen
physics-based computational models and grid resolutions. An ongoing problem in
the field is how to simulate these systems faster but with sufficient accuracy.
While many approaches involve simplified models of the underlying physics,
others are model-free and make predictions based only on existing simulation
data. We present a novel model-free approach in which we reformulate the
simulation problem to effectively increase the size of constrained pre-computed
datasets and introduce a novel neural network architecture (called a cluster
network) with an inductive bias well-suited to highly nonlinear computational
fluid dynamics solutions. Compared to the state-of-the-art in model-based
approximations, we show that our approach is nearly as accurate, an order of
magnitude faster, and easier to apply. Furthermore, we show that our method
outperforms other model-free approaches
Relaxed Softmax for learning from Positive and Unlabeled data
In recent years, the softmax model and its fast approximations have become
the de-facto loss functions for deep neural networks when dealing with
multi-class prediction. This loss has been extended to language modeling and
recommendation, two fields that fall into the framework of learning from
Positive and Unlabeled data. In this paper, we stress the different drawbacks
of the current family of softmax losses and sampling schemes when applied in a
Positive and Unlabeled learning setup. We propose both a Relaxed Softmax loss
(RS) and a new negative sampling scheme based on Boltzmann formulation. We show
that the new training objective is better suited for the tasks of density
estimation, item similarity and next-event prediction by driving uplifts in
performance on textual and recommendation datasets against classical softmax.Comment: 9 pages, 5 figures, 2 tables, published at RecSys 201