Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Ojeda, Cesar"'
Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces. These processes find wide application in the natural sciences and machine learning, but their inference is known to be
Externí odkaz:
http://arxiv.org/abs/2406.06419
Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by Williamson et al. (2010), such models implicitly assume that the probability of a topic to be active and its propor
Externí odkaz:
http://arxiv.org/abs/2301.10988
Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. Yet, it is known that their performance can drastically drop when there is a distribution shift between the data used during train
Externí odkaz:
http://arxiv.org/abs/2211.00384
Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly. In this work we introduce a novel neural language model that enforces, via inductive biases, explicit relational stru
Externí odkaz:
http://arxiv.org/abs/2207.03777
A novel first-order autoregressive moving average model for analyzing discrete-time series observed at irregularly spaced times is introduced. Under Gaussianity, it is established that the model is strictly stationary and ergodic. In the general case
Externí odkaz:
http://arxiv.org/abs/2203.16281
Autor:
Artiles Medina, Alberto, Mínguez Ojeda, César, Subiela Henríquez, José Daniel, Muriel García, Alfonso, Sánchez González, Álvaro, Mata Alcaraz, Marina, Brasero Burgos, Jennifer, Gajate Borau, Pablo, Gómez Dos Santos, Victoria, Jiménez Cidre, Miguel Ángel, Burgos Revilla, Francisco Javier
Publikováno v:
In Clinical Genitourinary Cancer December 2024 22(6)
Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of te
Externí odkaz:
http://arxiv.org/abs/2110.14747
Autor:
Mínguez Ojeda, César, Gómez Dos Santos, Victoria, Álvaro Lorca, Javier, Ruz-Caracuel, Ignacio, Pian, Héctor, Sanjuanbenito Dehesa, Alfonso, Gutiérrez Gutiérrez, Elvira, Sanz Miguelañez, Juan Luis, Pozo Mengual, Bernabé, Burgos Revilla, Francisco Javier, Araujo-Castro, Marta
Publikováno v:
In Annales d'Endocrinologie April 2024 85(2):104-109
Traditionally, Hawkes processes are used to model time--continuous point processes with history dependence. Here we propose an extended model where the self--effects are of both excitatory and inhibitory type and follow a Gaussian Process. Whereas pr
Externí odkaz:
http://arxiv.org/abs/2105.09618