Search Results for author: Maxim Borisyak

Found 14 papers, 4 papers with code

Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations

no code implementations1 Feb 2024 Christoph Lange, Isabel Thiele, Lara Santolin, Sebastian L. Riedel, Maxim Borisyak, Peter Neubauer, M. Nicolas Cruz Bournazou

This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training.

Data Augmentation

Latent State Space Extension for interpretable hybrid mechanistic models

no code implementations6 Dec 2023 Judit Aizpuru, Maxim Borisyak, Peter Neubauer, M. Nicolas Cruz Bournazou

We demonstrate the framework's capabilities by fitting a hybrid model based on a simple mechanistic growth model for E. coli with data generated in-silico by a much more complex one and identifying missing kinetics.

Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries

no code implementations4 Dec 2023 Maxim Borisyak, Stefan Born, Peter Neubauer, Mariano Nicolas Cruz-Bournazou

The method is agnostic to the particular nature of the time series and can be adapted for any task, for example, online monitoring, predictive control, design of experiments, etc.

Imputation Irregular Time Series +1

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development

no code implementations2 Sep 2022 Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese Schermeyer, Katharina Paulick, Maxim Borisyak, Mariano Nicolas Cruz-Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme, Peter Neubauer, Ernesto Martinez

ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks.

Model Selection Probabilistic Programming

NFAD: Fixing anomaly detection using normalizing flows

1 code implementation19 Dec 2019 Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach

Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i. e. focus on separating normal data from the rest of the space.

Bayesian Inference BIG-bench Machine Learning +4

Adaptive Divergence for Rapid Adversarial Optimization

1 code implementation1 Dec 2019 Maxim Borisyak, Tatiana Gaintseva, Andrey Ustyuzhanin

Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and the other is defined by a generator.

Machine Learning on sWeighted Data

no code implementations17 Oct 2019 Maxim Borisyak, Nikita Kazeev

Data analysis in high energy physics has to deal with data samples produced from different sources.

BIG-bench Machine Learning

$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

1 code implementation14 Jun 2019 Maxim Borisyak, Artem Ryzhikov, Andrey Ustyuzhanin, Denis Derkach, Fedor Ratnikov, Olga Mineeva

We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples.

Anomaly Detection General Classification

Machine Learning on data with sPlot background subtraction

1 code implementation28 May 2019 Maxim Borisyak, Nikita Kazeev

In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all.

BIG-bench Machine Learning

Numerical optimization for Artificial Retina Algorithm

no code implementations25 Sep 2017 Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach, Mikhail Belous

High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point.

Towards automation of data quality system for CERN CMS experiment

no code implementations25 Sep 2017 Maxim Borisyak, Fedor Ratnikov, Denis Derkach, Andrey Ustyuzhanin

Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken.

BIG-bench Machine Learning

Muon Trigger for Mobile Phones

no code implementations25 Sep 2017 Maxim Borisyak, Michail Usvyatsov, Michael Mulhearn, Chase Shimmin, Andrey Ustyuzhanin

The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays.

Cannot find the paper you are looking for? You can Submit a new open access paper.