jm + unsupervised-learning   3

Unsupervised machine translation: A novel approach to provide fast, accurate translations for more languages – Facebook Code
Training an MT model without access to any translation resources at training time (known as unsupervised translation) was the necessary next step. Research we are presenting at EMNLP 2018 outlines our recent accomplishments with that task. Our new approach provides a dramatic improvement over previous state-of-the-art unsupervised approaches and is equivalent to supervised approaches trained with nearly 100,000 reference translations. To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field; our methods showed an improvement of more than 10 BLEU points.

This is an important finding for MT in general and especially for the majority of the 6,500 languages in the world for which the pool of available translation training resources is either nonexistent or so small that it cannot be used with existing systems. For low-resource languages, there is now a way to learn to translate between, say, Urdu and English by having access only to text in English and completely unrelated text in Urdu – without having any of the respective translations.
unsupervised-learning  ml  machine-learning  ai  translation  facebook 
19 days ago by jm
Fast Forward Labs: Fashion Goes Deep: Data Science at Lyst
this is more than just data science really -- this is proper machine learning, with deep learning and a convolutional neural network. serious business
lyst  machine-learning  data-science  ml  neural-networks  supervised-learning  unsupervised-learning  deep-learning 
december 2015 by jm
'Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm' [PDF]
'Unsupervised anomaly detection is the process of finding outliers in data sets without prior training. In this paper, a histogram-based outlier detection (HBOS) algorithm is presented, which scores records in linear time. It assumes independence of the features making it much faster than multivariate approaches at the cost of less precision. A comparative evaluation on three UCI data sets and 10 standard algorithms show, that it can detect global outliers as reliable as state-of-the-art algorithms, but it performs poor on local outlier problems. HBOS is in our experiments up to 5 times faster than clustering based algorithms and up to 7 times faster than nearest-neighbor based methods.'
histograms  anomaly-detection  anomalies  machine-learning  algorithms  via:paperswelove  outliers  unsupervised-learning  hbos 
november 2014 by jm

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