72 citations · 320 across the 36 of their papers we have counts for
8 papers · 1 filter
Power- and Fragmentation-aware Online Scheduling for GPU Datacenters
Francesco Lettich, Emanuele Carlini, Franco Maria Nardini +2
The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs,…
Rewriting Conversational Utterances with Instructed Large Language Models
Elnara Galimzhanova, Cristina Ioana Muntean, Franco Maria Nardini +2
Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization…
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…
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challen…
Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search
Sebastian Bruch, Aditya Krishnan, Franco Maria Nardini
Clustering-based nearest neighbor search is an effective method in which points are partitioned into geometric shards to form an index, with only a few shards searched during query…
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable…
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini +1
A critical piece of the modern information retrieval puzzle is approximate nearest neighbor search. Its objective is to return a set of data points that are closest to a query…
Efficient Multi-Vector Dense Retrieval Using Bit Vectors
Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance…