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Distributed Representations of Words and Phrases and their Compositionality
TLDR
This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Efficient Estimation of Word Representations in Vector Space
TLDR
Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TLDR
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
TLDR
GNMT, Google's Neural Machine Translation system, is presented, which attempts to address many of the weaknesses of conventional phrase-based translation systems and provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delicited models.
Large Scale Distributed Deep Networks
TLDR
This paper considers the problem of training a deep network with billions of parameters using tens of thousands of CPU cores and develops two algorithms for large-scale distributed training, Downpour SGD and Sandblaster L-BFGS, which increase the scale and speed of deep network training.
Wide & Deep Learning for Recommender Systems
TLDR
Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow.
DeViSE: A Deep Visual-Semantic Embedding Model
TLDR
This paper presents a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text and shows that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training.
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
TLDR
This work proposes a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages using a shared wordpiece vocabulary, and introduces an artificial token at the beginning of the input sentence to specify the required target language.
Zero-Shot Learning by Convex Combination of Semantic Embeddings
TLDR
A simple method for constructing an image embedding system from any existing image classifier and a semantic word embedding model, which contains the $\n$ class labels in its vocabulary is proposed, which outperforms state of the art methods on the ImageNet zero-shot learning task.
Building high-level features using large scale unsupervised learning
TLDR
Contrary to what appears to be a widely-held intuition, the experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not.
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