1 citations · 5 across the 12 of their papers we have counts for
12 papers
Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting
Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1
Augmentation has become a central technique for improving deep forecasting models, but classification-style transformations tend to break the coherence between the look-back window…
LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
Ahmad Bdeir, Johannes Burchert, Tom Hanika +2
Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high in…
A Cross-Domain Benchmark for Active Learning
Thorben Werner, Johannes Burchert, Maximilian Stubbemann +1
Active Learning (AL) deals with identifying the most informative samples for labeling to reduce data annotation costs for supervised learning tasks. AL research suffers from the fa…
Robust Hyperbolic Learning with Curvature-Aware Optimization
Ahmad Bdeir, Johannes Burchert, Lars Schmidt-Thieme +1
Hyperbolic deep learning has become a growing research direction in computer vision due to the unique properties afforded by the alternate embedding space. The negative curvature a…
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi +3
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as wi…
Towards Comparable Active Learning
Thorben Werner, Johannes Burchert, Lars Schmidt-Thieme
Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data…
Forecasting Irregularly Sampled Time Series using Graphs
Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz +5
Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of…
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
Nghia Duong-Trung, Duc-Manh Nguyen, Danh Le-Phuoc
Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards exp…
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series
Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-thieme
Irregularly sampled time series data with missing values is observed in many fields like healthcare, astronomy, and climate science. Interpolation of these types of time series is…
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series
Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme
Asynchronous Time Series is a multivariate time series where all the channels are observed asynchronously-independently, making the time series extremely sparse when aligning them.…
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert +1
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experienc…
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung +2
Time series data is ubiquitous in research as well as in a wide variety of industrial applications. Effectively analyzing the available historical data and providing insights into…