Vlado Menkovski

Assistant Professor at @TUeindhoven

[Vlado Menkovski @vlamen]

Very excited to share this! Looking for candidates for a #PhD position on the intersection of #GenerativeAI and Material Science. Supervised by Prof. Sofia Calero and co-supervised by me. https://t.co/b49cN7PjiU

[Vlado Menkovski @vlamen]

An excellent opportunity to work on the intersection of ML and Physics. A vacancy on Machine Learning-based Simulation for Materials Discovery. Supervised by prof. Sofia Calero and co-supervised by me: https://t.co/VKq7TuSpUk

[Loek Tonnaer @LoekTonnaer]

For more details, we invite you to read the full paper, or come talk to us at ICML 2022!
10/10 🧵

[Loek Tonnaer @LoekTonnaer]

3) LSBD representations also satisfy previous disentanglement notions:
Good scores for D_LSBD (lower is better) typically imply good scores on traditional disentanglement metrics (higher is better), even if the reverse is not true.
9/

[Loek Tonnaer @LoekTonnaer]

2) LSBD-VAE and other LSBD methods *can* learn LSBD representations with limited supervision on transformations:
Methods that specifically target LSBD indeed score better on D_LSBD.
8/

[Loek Tonnaer @LoekTonnaer]

1) Traditional disentanglement methods don't learn LSBD representations:
Methods without a notion of the underlying symmetry group structure don't score well on D_LSBD, even if they do score well on traditional disentanglement metrics.
7/

[Loek Tonnaer @LoekTonnaer]

We perform experiments on a number of datasets with underlying symmetries, using our own LSBD-VAE and other LSBD-oriented methods, as well as various traditional disentanglement methods. From this we draw 3 main conclusions:
6/

[Loek Tonnaer @LoekTonnaer]

We show an intuitive justification of our D_LSBD metric, as well as its theoretical derivation. We also provide a practical implementation to compute D_LSBD for SO(2) symmetry groups of 2D rotations.
5/

[Loek Tonnaer @LoekTonnaer]

In our paper we present the following contributions:
i) D_LSBD, a well-formalized general metric to quantify LSBD in learned representations.
ii) LSBD-VAE, a weakly-supervised method to learn LSBD representations under certain assumptions.
4/

[Loek Tonnaer @LoekTonnaer]

However, there is currently no metric to quantify LSBD. Such a metric is crucial to evaluate methods aimed at learning LSBD, and to compare to previous notions of disentanglement.
3/

[Loek Tonnaer @LoekTonnaer]

Disentanglement is an important goal in representation learning, but there is no consensus on a formal definition. Higgins et al. (2018) propose such a definition based on the idea that real-world symmetries provide a structure to disentangle, which we refer to as LSBD.
2/

[Loek Tonnaer @LoekTonnaer]

Our paper "Quantifying and Learning Linear Symmetry-Based Disentanglement" got accepted at #ICML2022 @icmlconf!🥳🎉

Congrats to co-authors @tezcatlipocta (joint first), @vlamen, Mike Holenderski & Jim Portegies.

Paper: https://t.co/8EzL6zPkVi
Code: https://t.co/tkzVMWyglz
1/🧵

[Vlado Menkovski @vlamen]

The @FullyChargedShw visits Eindhoven and @SolarEHV to check out Stella Vita the solar camper https://t.co/PDeIapCh4N

[International Conference on Process Mining (ICPM) @icpm_conf]

Best Paper Award at #icpm21 goes to ....

Dominique Sommers, Vlado Menkovski and Dirk Fahland @dfahland with the paper Process Discovery using Graph Neural Networks

[Mitko Veta @mitkoveta]

We have a vacancy for PhD in Artificial Intelligence for Computational Pathology.

Goal: develop new methods for end-to-end training of DL models from whole slide images with application in oncology.

#digitalpathology #deeplearning #machinelearning #AI

https://t.co/w1wM7ll8oA

[Vlado Menkovski @vlamen]

Can Machine Learning be a driver for scientific discovery? Join our multidisciplinary team of scientists and help us push Machine Learning forward to drive progress in natural sciences! https://t.co/mpV7Is9bzN via @TU/e

[Alessandro Corbetta @alecorbetta]

Pedestrian orientation dynamics from high-fidelity measurements.
Open Access on Scientific Reports

https://t.co/6B8PsY6jL4
with J willems, @vlamen @ftoschi

[Alessandro Corbetta @alecorbetta]

New preprint: "Pedestrian orientation dynamics from high-fidelity measurements"

https://t.co/6tQP5Qkg4A

[microRNApapers @microRNA_papers]

Detection of pre-microRNA with Convolutional Neural Networks https://t.co/TYZASZrlIl

[Alessandro Corbetta @alecorbetta]

New preprint available 😀 "Deep learning velocity signals allows to quantify turbulence intensity"

https://t.co/LOXVlMASYa

with @vlamen, Roberto Benzi and @ftoschi