Search Results for author:
Found 7 papers, 2 papers with code
Searching Dense Representations with Inverted Indexes
In this work, we explore the contrarian approach of performing top-$k$ retrieval on dense vector representations using inverted indexes.
Vector Search with OpenAI Embeddings: Lucene Is All You Need
We provide a reproducible, end-to-end demonstration of vector search with OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test collection.
Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes
With Pyserini, which provides a Python interface to Anserini, users gain access to both sparse and dense retrieval models, as Pyserini implements bindings to the Faiss vector search library alongside Lucene inverted indexes in a uniform, consistent interface.
Effective Explanations for Entity Resolution Models
CERTA builds on a probabilistic framework that aims at computing the explanations evaluating the outcomes produced by using perturbed copies of the input records.
TrustyAI Explainability Toolkit
TrustyAI is an initiative which looks into explainable artificial intelligence (XAI) solutions to address this issue of explainability in the context of both AI models and decision services.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors
We demonstrate three approaches for adapting the open-source Lucene search library to perform approximate nearest-neighbor search on arbitrary dense vectors, using similarity search on word embeddings as a case study.
Affect Enriched Word Embeddings for News Information Retrieval
Distributed representations of words have shown to be useful to improve the effectiveness of IR systems in many sub-tasks like query expansion, retrieval and ranking.
