no code implementations • 12 Apr 2024 • Patricia A. Apellániz, Juan Parras, Santiago Zazo
Furthermore, our model offers enhanced flexibility by allowing the use of various differentiable distributions for individual features, making it possible to handle both continuous and discrete data types.
1 code implementation • 22 Dec 2023 • Patricia A. Apellániz, Juan Parras, Santiago Zazo
As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored medical data.
no code implementations • ICLR 2018 • Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo
This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.
no code implementations • 28 Oct 2017 • Sergio Valcarcel Macua, Aleksi Tukiainen, Daniel García-Ocaña Hernández, David Baldazo, Enrique Munoz de Cote, Santiago Zazo
We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL).
no code implementations • 30 Dec 2013 • Sergio Valcarcel Macua, Jianshu Chen, Santiago Zazo, Ali H. Sayed
We apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment.