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Nearest neighbor search AND Machine learning: techniques

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20 Documents

k-Nearest Neighbor lookups on embeddings: kNN vs SVM

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2023-04-15 About

[2009.13013] SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

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2023-04-06 About

Uri Alon sur Twitter : "K-nearest neighbors language models (kNN-LMs; @ukhndlwl et al., ICLR'2020) improve the perplexity of standard LMs... Why?"

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2023-01-11 About

Nearest Neighbor Indexes for Similarity Search | Pinecone

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2022-09-02 About

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

neulab/knn-transformers: PyTorch code for the RetoMaton paper: "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML 2022), including an implementation of kNN-LM and kNN-MT

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2022-07-21 About

Uri Alon sur Twitter : "New repo: kNN-Transformers..."

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2022-07-21 About

[2203.08913] Memorizing Transformers

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2022-05-07 About

A novel multi-label classification algorithm based on K-nearest neighbor and random walk - Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song, 2020

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2020-08-11 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

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

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

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2020-01-12 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

[1004.5370] Self-Taught Hashing for Fast Similarity Search

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2017-11-07 About