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Employment (4)

Freenome (United States): South San Francisco, California, US

2022-04-04 to present | Machine Learning Scientist
Employment
Source: Self-asserted source
Michael Widrich

Freenome (United States): South San Francisco, California, US

2021-10-01 to 2022-04-01 | Machine Learning Research Intern
Employment
Source: Self-asserted source
Michael Widrich

Johannes Kepler University of Linz: Linz, Oberösterreich, AT

2016-10 to 2022-04-01 | Research Assistant (ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning)
Employment
Source: Self-asserted source
Michael Widrich

Johannes Kepler University of Linz: Linz, Oberösterreich, AT

2016-10 to 2022-04-01 | Lecturer (Institute for Machine Learning)
Employment
Source: Self-asserted source
Michael Widrich

Works (11)

In silico proof of principle of machine learning-based antibody design at unconstrained scale

mAbs
2022-12-31 | Journal article
DOI: 10.1080/19420862.2022.2031482
Contributors: Rahmad Akbar; Philippe A. Robert; Cédric R. Weber; Michael Widrich; Robert Frank; Milena Pavlović; Lonneke Scheffer; Maria Chernigovskaya; Igor Snapkov; Andrei Slabodkin et al.
Source: check_circle
Crossref

Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Nature Computational Science
2022-12-19 | Journal article
DOI: 10.1038/s43588-022-00372-4
Contributors: Philippe A. Robert; Rahmad Akbar; Robert Frank; Milena Pavlović; Michael Widrich; Igor Snapkov; Andrei Slabodkin; Maria Chernigovskaya; Lonneke Scheffer; Eva Smorodina et al.
Source: check_circle
Crossref

In silico proof of principle of machine learning-based antibody design at unconstrained scale

2021-07-09 | Preprint
DOI: 10.1101/2021.07.08.451480
Contributors: Rahmad Akbar; Philippe A. Robert; Cédric R. Weber; Michael Widrich; Robert Frank; Milena Pavlović; Lonneke Scheffer; Maria Chernigovskaya; Igor Snapkov; Andrei Slabodkin et al.
Source: check_circle
Crossref

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

2021-07-08 | Preprint
DOI: 10.1101/2021.07.06.451258
Contributors: Philippe A. Robert; Rahmad Akbar; Robert Frank; Milena Pavlović; Michael Widrich; Igor Snapkov; Andrei Slabodkin; Maria Chernigovskaya; Lonneke Scheffer; Eva Smorodina et al.
Source: check_circle
Crossref

Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

SSRN Electronic Journal
2020 | Journal article
DOI: 10.2139/ssrn.3561442
Part of ISSN: 1556-5068
Source: Self-asserted source
Michael Widrich

Modern Hopfield Networks and Attention for Immune Repertoire Classification

Advances in Neural Information Processing Systems
2020-12-06 | Conference paper
DOI: 10.1101/2020.04.12.038158
ARXIV: arXiv:2007.13505v1
URI:

https://proceedings.neurips.cc/paper/2020/file/da4902cb0bc38210839714ebdcf0efc3-Paper.pdf

URI:

https://arxiv.org/abs/2007.13505
Source: Self-asserted source
Michael Widrich
grade
Preferred source (of 3)‎

Cross-Domain Few-Shot Learning by Representation Fusion

2020-10-13 | Preprint
ARXIV: arXiv:2010.06498v2
Source: Self-asserted source
Michael Widrich

Hopfield Networks is All You Need

2020-07-16 | Preprint
ARXIV: arXiv:2008.02217v2
Source: Self-asserted source
Michael Widrich

Explaining and Interpreting LSTMs

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
2019 | Book chapter
DOI: 10.1007/978-3-030-28954-6_11
Part of ISBN: 9783030289539
Part of ISBN: 9783030289546
Part of ISSN: 0302-9743
Part of ISSN: 1611-3349
Source: Self-asserted source
Michael Widrich

Rudder: Return decomposition for delayed rewards

Advances in Neural Information Processing Systems
2019 | Conference paper
Source: Self-asserted source
Michael Widrich

Speeding up semantic segmentation for autonomous driving

MLITS, NIPS Workshop
2016 | Conference paper
Source: Self-asserted source
Michael Widrich
How many people are using ORCID?