GitHub - facebookresearch/StarSpace: Learning embeddings for classification, retrieval and ranking.
StarSpace is a general-purpose neural model for efficient learning of entity embeddings for solving a wide variety of problems: Learning word, sentence or document level embeddings. Information retrieval: ranking of sets of entities/documents or objects, e.g. ranking web documents. Text classification, or any other labeling task. Metric/similarity learning, e.g. learning sentence or document simil
Introduction to Amazon SageMaker Object2Vec | AWS Machine Learning Blog
AWS Machine Learning Blog Introduction to Amazon SageMaker Object2Vec In this blog post, we’re introducing the Amazon SageMaker Object2Vec algorithm, a new highly customizable multi-purpose algorithm that can learn low dimensional dense embeddings of high dimensional objects. Embeddings are an important feature engineering technique in machine learning (ML). They convert high dimensional vectors i
Learning Entity Embeddings in one breath
Well maybe not, unless you’re routinely breaking the world-record in holding your breath! On the bright-side, entity embeddings is really as easy as it is fascinating to learn about. The core idea of using entity embeddings is to use a different set of dimension to represent a categorical set of data. A categorical set of inputs is a type of data where we have different categories (or types) that
Word embeddings in 2017: Trends and future directions
Word embeddings in 2017: Trends and future directions Word embeddings are an integral part of current NLP models, but approaches that supersede the original word2vec have not been proposed. This post focuses on the deficiencies of word embeddings and how recent approaches have tried to resolve them. This post discusses the deficiencies of word embeddings and how recent approaches have tried to res
Poincaré Embeddings for Learning Hierarchical Representations
2017-09-15 第9回最先端NLP勉強会
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