Johannes Lederer

Statistics, Machine Learning & Data Science

Menu

Skip to content
  • Home
  • Research
    • Team
    • Vignettes
    • Publications
  • Teaching
  • Book
  • Join/Contact

Category Archives: New Paper

Standard

Posted by

LedererLab

Posted on

December 20, 2022

Posted under

Accepted Paper, New Paper

New Paper in AStA Adv. Stat. Anal.

We have a new paper with the title “Statistical guarantees for sparse deep learning” in AStA Adv. Stat. Anal.

Standard

Posted by

LedererLab

Posted on

May 11, 2022

Posted under

New Paper

Approximate Stationary Points of Simple Neural Networks

Our new paper “Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks” is now online. Well done, Mahsa and Fang! 🎉🎉

Standard

Posted by

LedererLab

Posted on

February 3, 2022

Posted under

New Paper

New Paper on Variable Clustering

We have a new paper “VC-PCR: a prediction method based on supervised variable selection and clustering” about variable clustering in transcriptomics and in general. Great work especially from Rebecca, who just defended her PhD, and from Rainer and Bernadette, the two other members of our Belgian🇧🇪-American🇺🇸-German🇩🇪 tag team!

Standard

Posted by

LedererLab

Posted on

January 16, 2022

Posted under

New Paper

Depth Normalization of Small RNA Sequencing

Our paper “Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method” is now available on arXiv. Great to work with you, Yannick and Li-Xuan! 🦠

Standard

Posted by

LedererLab

Posted on

July 16, 2021

Posted under

New Paper

Normalizing Flows

We have submitted a new paper “copula-based normalizing flows.” Great to work with you, Mike and Asja! ☄️

Standard

Posted by

LedererLab

Posted on

June 7, 2021

Posted under

New Paper

Vanishing gradients

We have analized the vanishing-gradient problem in deep learning and potential remedies here. Congratulations, Leni! 🏄

Standard

Posted by

LedererLab

Posted on

June 1, 2021

Posted under

New Paper

Two new papers on deep learning

We two new papers on deep learning, one on targeted deep learning and one on robust deep learning. Well done, Shih-Ting!

Standard

Posted by

LedererLab

Posted on

January 27, 2021

Posted under

New Paper

Activation Functions in Artificial Neural Networks

We have composed an overview of activation functions in artificial neural networks here.

Standard

Posted by

LedererLab

Posted on

October 6, 2020

Posted under

New Paper

Optimization landscapes in deep learning

We analyze the optimization landscapes of feedforward neural networks here. We show especially that the landscapes of wide networks do not have spurious local minima.

Standard

Posted by

LedererLab

Posted on

September 22, 2020

Posted under

New Paper

Statistics and artificial intelligence

We have discussed the role of statistics in artificial intelligence here.

Post navigation

← Older posts

Book

Archives

  • January 2023
  • December 2022
  • September 2022
  • August 2022
  • July 2022
  • June 2022
  • May 2022
  • April 2022
  • February 2022
  • January 2022
  • December 2021
  • November 2021
  • October 2021
  • August 2021
  • July 2021
  • June 2021
  • May 2021
  • April 2021
  • March 2021
  • February 2021
  • January 2021
  • October 2020
  • September 2020
  • July 2020
  • June 2020
  • May 2020
  • April 2020
  • March 2020
  • February 2020
  • November 2019
  • September 2019
  • August 2019
  • July 2019
  • June 2019
  • March 2019
  • February 2019
  • January 2019
  • October 2018
  • June 2018
  • April 2018
  • March 2018
  • January 2018
  • November 2017
  • October 2017
  • September 2017
  • June 2017
  • April 2017
  • January 2017
  • December 2016
  • November 2016
  • October 2016
  • September 2016
  • August 2016
  • July 2016
Blog at WordPress.com.
Johannes Lederer
Blog at WordPress.com.
  • Follow Following
    • Johannes Lederer
    • Already have a WordPress.com account? Log in now.
    • Johannes Lederer
    • Customize
    • Follow Following
    • Sign up
    • Log in
    • Report this content
    • View site in Reader
    • Manage subscriptions
    • Collapse this bar
 

Loading Comments...