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タグ検索の該当結果が少ないため、タイトル検索結果を表示しています。
GitHub - raghavan/PdfGptIndexer: An efficient tool for indexing and searching PDF text data using OpenAI API and FAISS (Facebook AI Similarity Search) index, designed for rapid information retrieval and superior search accuracy.
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An Introduction to Neural Information Retrieval - Microsoft Research
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ supervised machine learning (ML) techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the ga
This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. You can order this book at CUP, at your local bookstore or on the internet. The best search term to use is the ISBN: 0521865719. The book aims to provide a modern approach to information retrieval from a compu
Information Retrieval and Web Search まとめ(1): コース概要・半構造化データ - stop-the-world
最初にこのコースの概要を軽く説明したあと、構造化データ、非構造化データ、半構造化データについて簡単にまとめる。 この講義で主に扱うのは、非構造化データと半構造化データ。 この記事は Information Retrieval and Web Search Advent Calendar 2020 の1日目の記事です。 adventar.org コース概要 スタンフォード大学の情報検索のコースで、 IIR や FSNLP の著者である Chris Manning 教授と、Google の Pandu Nayak 氏が講師。 このコースでは以下のようなトピックを扱っている： 基本的な検索システムの技術 テキストの効率的なインデキシング ブーリアンモデルとベクトル空間モデル 検索の評価とユーザインターフェース クローリングやリンク解析などの Web 技術 文書分類・クラスタリング ランク学習を含
GitHub - thunlp/OpenMatch: An Open-Source Package for Information Retrieval.
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Information Retrieval and Web Search Advent Calendar 2020 - Adventar
Information Retrieval and Web Search まとめ(1): コース概要・半構造化データ - stop-the-world
データベース検索秘匿化技術「Private Information Retrieval (PIR)」の最先端アルゴリズムの発明と特許・商標出願までの道のり - Qiita
データベース検索秘匿化技術「Private Information Retrieval (PIR)」の最先端アルゴリズムの発明と特許・商標出願までの道のり特許ElGamal暗号PIR商標 ブロックチェーンエンジニアのびりあるです。 データベースの検索内容をサーバ側に知られることなく検索ができる「Private Information Retrieval (以下PIR)」という暗号技術があるのですが、既存の実装ではシングルコアかつCPUでしか計算できるものがなく、実用的な範囲の計算時間では大規模化が難しいという課題がありました。 そこで、独自の研究開発を重ね、並列化がCPUもりも難しいGPUを用いて非常に高い効率で並列化ができるアルゴリズムを考案し、その試験実装を行った結果、家庭向けの安価なGPU (NVIDIA GeForce RTX 3080) 一台のみで、従来実装と比べて実実行時間性能
From Cranfield Experiments to Neural Information Retrieval
IntroductionWhen I was working in industry, I was seeing SOTA models published every week in various machine learning fields while I was struggling with a complex search application consisting of lots of parameters and heuristics. I naturally came to think “how can I incorporate recent research advances into practical applications?”. I had this question in my mind for a while and I ended up gettin
GitHub - NirDiamant/RAG_Techniques: This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich response
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GitHub - castorini/pyserini: Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with our group's Anserini IR toolkit, which is built on Lucene. Retrieval using dense representations is provided via integration with Facebook's Faiss library. Pyserini is primarily designed to provide effective, rep
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Introduction to Information Retrieval
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