no code implementations • 23 Feb 2024 • Alfredo De la Fuente, Saurabh Singh, Johannes Ballé
We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis.
1 code implementation • 15 Oct 2021 • Ludovic Denoyer, Alfredo De la Fuente, Song Duong, Jean-Baptiste Gaya, Pierre-Alexandre Kamienny, Daniel H. Thompson
SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms.
no code implementations • 22 May 2018 • Lukas Mosser, Wouter Kimman, Jesper Dramsch, Steve Purves, Alfredo De la Fuente, Graham Ganssle
Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive.