User profiles for Felix Divo
Felix DivoTU Darmstadt, Germany Verified email at cs.tu-darmstadt.de Cited by 748 |
Tslearn, a machine learning toolkit for time series data
tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering, …
for pre-processing and feature extraction as well as dedicated models for clustering, …
Forecasting Company Fundamentals
F Divo, E Endress, K Endler, K Kersting… - arXiv preprint arXiv …, 2024 - arxiv.org
Company fundamentals are key to assessing companies' financial and overall success and
stability. Forecasting them is important in multiple fields, including investing and …
stability. Forecasting them is important in multiple fields, including investing and …
The Constitutional Filter
Predictions in environments where a mix of legal policies, physical limitations, and
operational preferences impacts an agent's motion are inherently difficult. Since Neuro-Symbolic …
operational preferences impacts an agent's motion are inherently difficult. Since Neuro-Symbolic …
Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation
Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders,
present a significant challenge in machine learning and AI, critically affecting model …
present a significant challenge in machine learning and AI, critically affecting model …
United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once
In natural language processing and vision, pretraining is utilized to learn effective representations.
Unfortunately, the success of pretraining does not easily carry over to time series due …
Unfortunately, the success of pretraining does not easily carry over to time series due …
Graph Neural Networks Need Cluster-Normalize-Activate Modules
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured
data. Despite their successful and diverse applications, oversmoothing prohibits deep …
data. Despite their successful and diverse applications, oversmoothing prohibits deep …
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Time series data is prevalent across numerous fields, necessitating the development of robust
and accurate forecasting models. Capturing patterns both within and between temporal …
and accurate forecasting models. Capturing patterns both within and between temporal …
Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
Causality in time series can be difficult to determine, especially in the presence of non-linear
dependencies. The concept of Granger causality helps analyze potential relationships …
dependencies. The concept of Granger causality helps analyze potential relationships …
probabilists/zuko: Zuko 1.1. 0
F Rozet, F Divo, S Schnake - Zenodo, 2024 - ui.adsabs.harvard.edu
✨ What's new New VAE tutorial using the MNIST dataset (8812e04507bf27d4fb9346acd9174459c097fc62)
Add support for unconditional univariate flows (# 34) New Bernstein …
Add support for unconditional univariate flows (# 34) New Bernstein …
The influence of divorce on men's health
DS Felix, WD Robinson, KJ Jarzynka - Journal of Men's Health, 2013 - liebertpub.com
… In this case report we review current literature on the sequelae of divorce on men's health,
and highlight key features of divorce from a multi-disciplinary lens using the example of a 45-…
and highlight key features of divorce from a multi-disciplinary lens using the example of a 45-…