Xavier ALAMEDA-PINEDA
Author's posts
Feb 03
Seminar: Transfer Learning, Data Efficiency and Fairness in Deep Reinforcement Learning
Seminar by Matthieu Zimmer, UM-SJTU Monday, February 8th, 11:00 – 12:00 INRIA Montbonnot Saint-Martin Abstract: In reinforcement learning, we aim at designing agents that take sequential decisions in unknown environments by learning through their own interaction with such environments. However, learning from scratch is often costly in terms of data to collect, transfer learning can help …
Jan 25
Seminar: Complex-valued and hybrid models for audio processing
Seminar by Paul Magron, IRIT Tuesday, February 2nd, 16:00 – 17:00 INRIA Montbonnot Saint-Martin Abstract: In this talk, I will give an overview of my work, which main application is sound source separation, the task of automatically extracting constitutive components from their observed mixture in an audio recording. I will address it in the time-frequency domain, …
Nov 13
Seminar: Using cognitive science for artificial intelligence
Seminar by Chris Reinke, Inria Bordeaux Tuesday, December 10th 2019, 11:00 – 12:00, room F107 INRIA Montbonnot Saint-Martin Abstract: Humans show impressive learning capabilities, allowing us to adapt efficiently to new and diverse tasks. In artificial intelligence we want artificial systems such as robots to have similar learning abilities. In this talk, I want to …
Nov 07
[Closed] Master Internship on Audio-visual speech separation using variational auto-encoders
Topic: In this Master thesis, we address the problem of speech separation given single-channel microphone mixed speech and video frames of the involved speakers. Although there exist several audio-only speech separation methods [1], here, we aim to utilize also the visual information, that is, video frames of speakers’ lips. This would help to distinguish different …
Oct 08
[Closed] Master Internship on Robust Deep Regression
Topic: In this Master thesis we address the problem of how to robustly train a ConvNet for regression, or deep robust regression [1,2]. Traditionally, deep regression employs the L2 loss function [3], known to be sensitive to outliers, i.e. samples that either lie at an abnormal distance away from the majority of the training samples, …
Oct 08
[Closed] Researcher on Deep and Reinforcement Learning for Robotics
Starting Date: February 1st, 2020. Funding: The H2020 ICT SPRING Project Contact Point: Xavier Alameda-Pineda Duration: From 2 and up to 4 years. To apply: https://jobs.inria.fr/public/classic/fr/offres/2019-02083 General Context: SPRING – Socially Pertinent Robots in Gerontological Healthcare – is a 4-year R&D project fully funded by the European Comission under the H2020 framework. SPRING aims to develop …
Oct 08
[Closed] Engineer on Deep Learning and Cloud Computing
Starting Date:November 1st, 2019 – February 1st, 2020. Funding: The H2020 ICT SPRING Project Contact Point: Xavier Alameda-Pineda Duration: 2 years and up to 4 years. To apply: https://jobs.inria.fr/public/classic/fr/offres/2019-02081 General Context: SPRING – Socially Pertinent Robots in Gerontological Healthcare – is a 4-year R&D project fully funded by the European Comission under the H2020 framework. SPRING …
Oct 08
[Closed] Engineer on Deep Learning and Robotics
Starting Date: November 1st, 2019 – February 1st, 2020. Duration: 2 years and up to 4 years. Funding: The H2020 ICT SPRING Project Contact Point: Xavier Alameda-Pineda To apply: https://jobs.inria.fr/public/classic/fr/offres/2019-02082 General Context: SPRING – Socially Pertinent Robots in Gerontological Healthcare – is a 4-year R&D project fully funded by the European Comission under the H2020 framework. …
Sep 27
H2020 Project SPRING awarded!
The Perception team is happy to announce that a new project has been awarded by the European Union under the H2020-ICT program. The main objective of SPRING (Socially Pertinent Robots in Gerontological Healthcare) is the development of socially assistive robots with the capacity of performing multimodal multiple-person interaction and open-domain dialogue. In more detail: The scientific objective of …
Oct 10
(Closed) MSc. Project on Speaker identity modeling with deep learning for re-identification
MSc. Project on Speaker identity modeling with deep learning for re-identification Short description: Speaker identification is the task that aims at determining which speaker has produced a given utterance [1]. On the other hand, speaker verification or re-identification aims at determining whether there is a match between a given speech utterance and a target speaker …
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