From df657fa6e6add1b99bc2aeeadb6be7dfb11f9bc8 Mon Sep 17 00:00:00 2001 From: SENACHERIBBE Andrea <andrea.senacheribbe@inria.fr> Date: Mon, 11 Jan 2021 09:52:07 +0100 Subject: [PATCH] fix links on website --- federated_learning/introduction.md | 26 ++++++++++++++------------ heterogeneous_data/introduction.md | 2 +- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/federated_learning/introduction.md b/federated_learning/introduction.md index a6d16b9..3c1199a 100644 --- a/federated_learning/introduction.md +++ b/federated_learning/introduction.md @@ -1,15 +1,3 @@ -## Links - -[Presentation material](https://ecaad164-c957-4008-a451-5e1098ff8953.filesusr.com/ugd/68a50d_a3d074241b3a4342be2fef2413ee61c7.pdf) - -[Colab notebook - part 1](https://colab.research.google.com/drive/1_uemRwNuok1wop6wP2Aiokn0KQgcwfr1?usp=sharing) - -[Colab notebook - part 2](https://colab.research.google.com/drive/1PiUee4n8T7pIhDV5zDEqhsK5jXvDYHpO?usp=sharing) - -[Colab notebook - part 3](https://colab.research.google.com/drive/1kIbrUtNH_WIPQX5vLyzRjs5CTgKA2CMT?usp=sharing) - -[Colab notebook - part 4](https://colab.research.google.com/drive/10wEN9eqdE9Z7CtvhRFgsL3gAzunZGlee?usp=sharing) - # Introduction Standard machine learning approaches require to have a centralizaed dataset in order to train a model. In certain scenarios like in the biomedical field, this is not straightforward due to several reasons like: @@ -58,6 +46,20 @@ The main challenges in FL are associated to: - **Security:** adversarial attacks and data leakage. + +## Links + +[Presentation material](https://ecaad164-c957-4008-a451-5e1098ff8953.filesusr.com/ugd/68a50d_a3d074241b3a4342be2fef2413ee61c7.pdf) + +[Colab notebook - part 1](https://colab.research.google.com/drive/1_uemRwNuok1wop6wP2Aiokn0KQgcwfr1?usp=sharing) + +[Colab notebook - part 2](https://colab.research.google.com/drive/1PiUee4n8T7pIhDV5zDEqhsK5jXvDYHpO?usp=sharing) + +[Colab notebook - part 3](https://colab.research.google.com/drive/1kIbrUtNH_WIPQX5vLyzRjs5CTgKA2CMT?usp=sharing) + +[Colab notebook - part 4](https://colab.research.google.com/drive/10wEN9eqdE9Z7CtvhRFgsL3gAzunZGlee?usp=sharing) + + --- ## References diff --git a/heterogeneous_data/introduction.md b/heterogeneous_data/introduction.md index 6cc9a52..4176ec3 100644 --- a/heterogeneous_data/introduction.md +++ b/heterogeneous_data/introduction.md @@ -2,7 +2,7 @@ This lecture aims at covering the statistical background required to perform association analysis in typical studies of heterogeneous information. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group. -Links: +## Links: - [Presentation material](https://marcolorenzi.github.io/material/AI4Health_winter_school_part1.pdf). -- GitLab