Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Sgherzi, Francesco"'
Autor:
Esmaili-Dokht, Pouya, Sgherzi, Francesco, Girelli, Valeria Soldera, Boixaderas, Isaac, Carmin, Mariana, Monemi, Alireza, Armejach, Adria, Mercadal, Estanislao, Llort, German, Radojkovic, Petar, Moreto, Miquel, Gimenez, Judit, Martorell, Xavier, Ayguade, Eduard, Labarta, Jesus, Confalonieri, Emanuele, Dubey, Rishabh, Adlard, Jason
The Memory stress (Mess) framework provides a unified view of the memory system benchmarking, simulation and application profiling. The Mess benchmark provides a holistic and detailed memory system characterization. It is based on hundreds of measure
Externí odkaz:
http://arxiv.org/abs/2405.10170
Autor:
Centurion, Diego Ihara, Chubarian, Karine, Fan, Bohan, Sgherzi, Francesco, Radhakrishnan, Thiruvenkadam S, Sidiropoulos, Anastasios, Straight, Angelo
We propose a label poisoning attack on geometric data sets against $k$-nearest neighbor classification. We provide an algorithm that can compute an $\varepsilon n$-additive approximation of the optimal poisoning in $n\cdot 2^{2^{O(d+k/\varepsilon)}}$
Externí odkaz:
http://arxiv.org/abs/2306.12377
Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommender systems. The performance of sparse kernels varies greatly depending on the structure of the input matrix, makin
Externí odkaz:
http://arxiv.org/abs/2304.06944
Graph analytics techniques based on spectral methods process extremely large sparse matrices with millions or even billions of non-zero values. Behind these algorithms lies the Top-K sparse eigenproblem, the computation of the largest eigenvalues and
Externí odkaz:
http://arxiv.org/abs/2201.07498
Publikováno v:
In Journal of Parallel and Distributed Computing October 2024 192
Large-scale eigenvalue computations on sparse matrices are a key component of graph analytics techniques based on spectral methods. In such applications, an exhaustive computation of all eigenvalues and eigenvectors is impractical and unnecessary, as
Externí odkaz:
http://arxiv.org/abs/2103.10040
Sparse matrix-vector multiplication is often employed in many data-analytic workloads in which low latency and high throughput are more valuable than exact numerical convergence. FPGAs provide quick execution times while offering precise control over
Externí odkaz:
http://arxiv.org/abs/2009.10443
Autor:
Fan, Bohan, Centurion, Diego Ihara, Mohammadi, Neshat, Sgherzi, Francesco, Sidiropoulos, Anastasios, Valizadeh, Mina
We study the problem of finding a mapping $f$ from a set of points into the real line, under ordinal triple constraints. An ordinal constraint for a triple of points $(u,v,w)$ asserts that $|f(u)-f(v)|<|f(u)-f(w)|$. We present an approximation algori
Externí odkaz:
http://arxiv.org/abs/2004.13202
Learning Mahalanobis metric spaces is an important problem that has found numerous applications. Several algorithms have been designed for this problem, including Information Theoretic Metric Learning (ITML) [Davis et al. 2007] and Large Margin Neare
Externí odkaz:
http://arxiv.org/abs/1905.09989
Autor:
Sgherzi, Francesco
Algorithms relying on Ordinal Informations to retrieve embedding have seen rise in usage in recent times due to the increase of popularity of automated analyses involving human generated opinions. In addition to this applications have been proven to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d30ab72116882e1d447285e5fe33c27