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20202025
most citedDistilled Neural Networks for Efficient Learning to Rank

21 citations · 26 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.IR2025

Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices

Patrizio Dazzi, William Guglielmo, Franco Maria Nardini +2

This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support…

cs.IR2025

Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades

Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto +1

We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense ne…

cs.IR2024★ 3 cited

Early Exit Strategies for Approximate k-NN Search in Dense Retrieval

Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3

Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approxim…

cs.IR2021

Learning Early Exit Strategies for Additive Ranking Ensembles

Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the…

cs.IR2020

Query-level Early Exit for Additive Learning-to-Rank Ensembles

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers…