FG Maschinelles Lernen

46 Items

Recent Submissions
Exploring density functional subspaces with genetic algorithms

Gastegger, Michael ; González, Leticia ; Marquetand, Philipp (2018-12-14)

We use a genetic algorithm to explore the subspace of combination and parametrization patterns spanned by a set of popular exchange and correlation functional approximations. Using the well-balanced GMTKN30 benchmark database to guide the evolutionary process, we find that the genetic algorithm is able to recover variants of several popular generalized gradient approximation functionals and hyb...

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Westermayr, Julia ; Gastegger, Michael ; Marquetand, Philipp (2020-04-20)

In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties—multiple energies, forces, and different couplings—for photodynamics simulations. We simplify such simulations substantially by (i) a phase-...

Machine learning enables long time scale molecular photodynamics simulations

Westermayr, Julia ; Gastegger, Michael ; Menger, Maximilian F. S. J. ; Mai, Sebastian ; González, Leticia ; Marquetand, Philipp (2019-08-05)

Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of re...

Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings

Brandl, Stephanie ; Lassner, David (2019-08-02)

We propose Word Embedding Networks, a novel method that is able to learn word embeddings of individual data slices while simultaneously aligning and ordering them without feeding temporal information a priori to the model. This gives us the opportunity to analyse the dynamics in word embeddings on a large scale in a purely data-driven manner. In experiments on two different newspaper corpora, t...

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Schütt, Kristof T. ; Gastegger, Michael ; Tkatchenko, Alexandre ; Müller, Klaus-Robert (2019-09-10)

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler–Parrinello networks as well as the end-to-end model SchNet. Both model...

Molecular Dynamics with Neural Network Potentials

Gastegger, Michael ; Marquetand, Philipp (2020-06-04)

Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine learning techniques can help to overcome these limitations by providing access...

Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions""

Schütt, Kristof T. ; Gastegger, Michael ; Tkatchenko, Alexandre ; Müller, Klaus-Robert ; Maurer, Reinhard J. (2019-09-25)

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analys...

Similarity encoder: A neural network architecture for learning similarity preserving embeddings

Horn, Franziska (2020)

Matrix factorization is at the heart of many machine learning algorithms, for example, for dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative filtering. Understanding a singular value decomposition (SVD) of a matrix as a neural network optimization problem enables us to decompose large matrices efficiently while dealing naturally with missing values in th...

Corona Twitter Dataset: 16 February 2020 - 03 March 2020

Brandl, Stephanie ; Lassner, David (2020)

For this dataset we have downloaded 22376075 tweets in 64 languages from 16 February until 03 March 2020. We used the following keywords: CORONA, CORONAVIRUS and #COVID-19.

BCI under distraction: Motor imagery in a pseudo realistic environment

Brandl, Stephanie (2015)

With the aim to investigate BCI in a pseudo-realistic environment, we recorded EEG from 16 healthy participants. Participants were asked to perform motor imagery tasks while dealing with different types of distractions such as vibratory stimulations or listening tasks.

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

Han, Chang-Hee ; Müller, Klaus-Robert ; Hwang, Han-Jeong (2020-03-02)

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic becaus...

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

von Lühmann, Alexander ; Ortega-Martinez, Antonio ; Boas, David A. ; Yücel, Meryem Ayşe (2020-02-18)

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal ...

Towards more efficient and performant computations in quantum chemistry with machine learning

Pronobis, Wiktor (2020)

Kernel methods allow an efficient solution of highly non-linear regression problems often encountered in quantum chemistry. Due to its flexibility it is unclear how to design a similarity matrix represented by the kernel which encodes a given learning problem in a compact and beneficial way. In this thesis, we propose novel kernels for quantum mechanical systems which are composed of two- and t...

Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment

Brandl, Stephanie ; Höhne, Johannes ; Müller, Klaus-Robert ; Samek, Wojciech (2015-07-02)

Bringing Brain-Computer Interfaces (BCIs) into everyday life is a challenge because an out-of-lab environment implies the presence of variables that are largely beyond control of the user and the software application. This can severely corrupt signal quality as well as reliability of BCI control. Current BCI technology may fail in this application scenario because of the large amounts of noise,...

Brain–computer interfacing under distraction: an evaluation study

Brandl, Stephanie ; Frølich, Linda ; Höhne, Johannes ; Müller, Klaus-Robert ; Samek, Wojciech (2016-08-31)

Objective. While motor-imagery based brain–computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of...

Robust artifactual independent component classification for BCI practitioners

Winkler, Irene ; Brandl, Stephanie ; Horn, Franziska ; Waldburger, Eric ; Allefeld, Carsten ; Tangermann, Michael (2014-05-19)

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach....

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Thomas, Armin W. ; Heekeren, Hauke R. ; Müller, Klaus-Robert ; Samek, Wojciech (2019-12-10)

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framewor...

Evaluation of a Compact Hybrid Brain-Computer Interface System

Shin, Jaeyoung ; Müller, Klaus-Robert ; Schmitz, Christoph H. ; Kim, Do-Won ; Hwang, Han-Jeong (2017-03-08)

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involve...

Brain Oscillations and Functional Connectivity during Overt Language Production

Ewald, Arne ; Aristei, Sabrina ; Nolte, Guido ; Rahman, Rasha Abdel (2012-06-07)

In the present study we investigate the communication of different large scale brain sites during an overt language production task with state of the art methods for the estimation of EEG functional connectivity. Participants performed a semantic blocking task in which objects were named in semantically homogeneous blocks of trials consisting of members of a semantic category (e.g., all objects...

Powerful Statistical Inference for Nested Data Using Sufficient Summary Statistics

Dowding, Irene ; Haufe, Stefan (2018-03-19)

Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-lev...