Semanlink - Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations

Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations – Google Research (WWW 2020)

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sl:bookmarkOf : https://research.google/pubs/pub50257/
sl:creationDate : 2021-11-04
sl:creationTime : 2021-11-04T17:31:42Z
sl:relatedDoc : http://www.semanlink.net/doc/2021/10/next_gen_sentence_embeddings_wi

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> We show that it is feasible to perform **entity
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Contributions:

> -  a dual encoder architecture for
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[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge

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> a neural language model that includes **an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge.**... **The model can be updated without re-training by manipulating its symbolic representations**. In particular this model allows us to add new facts and overwrite existing ones.

> a **neural language model which learns to access information
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> This
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>
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> we include an additional
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TODO:

- read again IBM's [Span Selection Pre-training for Question Answering](doc:2019/09/_1909_04120_span_selection_pre) ("an effort to avoid encoding general knowledge in the transformer network itself")
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2019-07-31 A propos

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