Semanlink - Big Data AND Machine learning

facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.

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2020-06-09 About

Thomas Wolf sur Twitter : "I liked the LSH attention in the reformer..."

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2020-01-05 About

Why and How we use Pangeo at CNES - pangeo - Medium

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2019-07-17 About

Deep Locality Sensitive Hashing

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2019-05-26 About

Semantic hashing (2008) - Ruslan Salakhutdinov, Geoffrey Hinton

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> We show how to learn a deep graphical model of the word-count vectors obtained from a
large set of documents. The values of the latent variables in the deepest layer are easy to
infer and give a much better representation of each document than Latent Semantic Analysis.
When the deepest layer is forced to use a small number of binary variables (e.g. 32),
the graphical model performs ‘‘semantic hashing”: Documents are mapped to memory
addresses in such a way that semantically similar documents are located at nearby
addresses. Documents similar to a query document can then be found by simply accessing
all the addresses that differ by only a few bits from the address of the query document. This
way of extending the efficiency of hash-coding to approximate matching is much faster
than locality sensitive hashing, which is the fastest current method. By using semantic
hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying
TF-IDF to the entire document set.

Indexing is implemented in the following manner: a document is mapped to a word-count vector and then this vector is passed through a [#Restricted Boltzmann Machine](/tag/restricted_boltzmann_machine) autoencoder and encoded to 32-bit address.

2017-11-07 About

Introducing DataFrames in Spark for Large Scale Data Science | Databricks

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