72 citations · 320 across the 36 of their papers we have counts for
5 papers · 1 filter
Bridging Dense and Sparse Maximum Inner Product Search
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- retrie…
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini +2
Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving the…
Neural Network Compression using Binarization and Few Full-Precision Weights
Franco Maria Nardini, Cosimo Rulli, Salvatore Trani +1
Quantization and pruning are two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression techniq…
Efficient and Effective Tree-based and Neural Learning to Rank
Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini
This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency…
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
Sebastian Bruch, Franco Maria Nardini, Amir Ingber +1
Maximum Inner Product Search or top-k retrieval on sparse vectors is well-understood in information retrieval, with a number of mature algorithms that solve it exactly. However, al…