Semanlink - NTIC ; Informatique ; Information retrieval AND Nearest neighbor search

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NTIC ; Informatique ; Information retrieval AND Nearest neighbor search

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16 Documents

Karl Higley sur Twitter : "Many ANN search tools (e.g. FAISS, ScaNN) allow you to provide multiple points as part of the same query..."

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2022-08-20 About

[2203.08913] Memorizing Transformers

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2022-05-07 About

How to Build a Semantic Search Engine With Transformers and Faiss | by Kostas Stathoulopoulos | Towards Data Science

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2022-01-29 About

James Briggs sur Twitter : *free* course on vector similarity search and Faiss..."

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2021-10-16 About

Finding similar documents with transformers · Codegram

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2020-07-10 About

[2007.00077] Similarity Search for Efficient Active Learning and Search of Rare Concepts

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2020-07-02 About

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

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2020-06-09 About

MinHash token filter | Elasticsearch Reference

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2020-02-14 About

ElastiK Nearest Neighbors

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2020-02-13 About

Building a Search Engine with BERT and TensorFlow - Towards Data Science

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2020-01-12 About

Building a real-time embeddings similarity matching system | Solutions | Google Cloud

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2020-01-11 About

Text similarity search in Elasticsearch using vector fields | Elastic Blog

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2020-01-10 About

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 About

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 About

[1703.03129] Learning to Remember Rare Events

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2018-10-23 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About