Keyword Analysis & Research: starspace
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StarSpace - Facebook
https://ai.meta.com/tools/starspace/
WebStarSpace. TOOLS. StarSpace is a general purpose neural embedding model that can be applied to many areas including text classification. GitHub. View Research. A multi-purpose learning model. StarSpace learns to represent objects of different types into a common vectorial embedding space in order to compare them against each other.
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GitHub - facebookresearch/StarSpace: Learning embeddings for
https://github.com/facebookresearch/StarSpace
WebStarSpace 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. …
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StarSpace Algorithm: Mining and Embedding User Interests
https://towardsdatascience.com/starspace-mining-and-embedding-user-interests-28081937f95
WebJul 20, 2019 · StarSpace is an algorithm proposed by Facebook [1]. StarSpace is a general-purpose neural model for efficient learning of entity embeddings for solving a wide variety of problems. So, the keywords are “general-purpose” and “entity embedding”. In other words, you can embed whatever you want by StarSpace, including the user.
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[1709.03856] StarSpace: Embed All The Things! - arXiv.org
https://arxiv.org/abs/1709.03856
WebSep 12, 2017 · StarSpace: Embed All The Things! We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and ...
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StarSpace: Embed All The Things! | Facebook AI Research
https://ai.meta.com/research/publications/starspace-embed-all-the-things/
WebFeb 2, 2018 · We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or…
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[1709.03856] StarSpace: Embed All The Things! - arXiv Vanity
https://ar5iv.labs.arxiv.org/html/1709.03856
WebMar 19, 2024 · We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or …
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StarSpace: Embed All The Things! - Meta Research
https://research.facebook.com/publications/starspace-embed-all-the-things/
WebFeb 2, 2018 · We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or …
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[Learning Note] StarSpace For Multi-label Text Classification
https://towardsdatascience.com/learning-note-starspace-for-multi-label-text-classification-81de0e8fca53
WebJan 21, 2018 · Published in. Towards Data Science. ·. 4 min read. ·. Jan 21, 2018. 2. StarSpace is an ambitious model that attempts to solve a wide range of entity-embedding-related problems. It has been created and open-sourced by Facebook AI Research (FAIR).
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StarSpace: Embed All The Things! - AAAI
https://aaai.org/papers/11996-starspace-embed-all-the-things/
WebFeb 8, 2018 · We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification,ranking tasks such as information retrieval/web search,collaborative filtering-based or content-based recommendation,embedding of multi-relational graphs, and learning word, sentence or …
DA: 94 PA: 61 MOZ Rank: 38
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arXiv:1709.03856v5 [cs.CL] 21 Nov 2017
https://arxiv.org/pdf/1709.03856.pdf
WebWe introduce StarSpace, a neural embedding model that is general enough to solve a wide variety of problems: Text classification, or other labeling tasks, e.g. sentiment classification. Ranking of sets of entities, e.g. ranking web documents given a query. Collaborative filtering-based recommendation, e.g. rec-ommending documents, music or videos.
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