1 citations · 3 across the 18 of their papers we have counts for
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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…
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Christian Kloetergens +3
Probabilistic forecasting models for joint distributions of targets in irregular time series with missing values are a heavily under-researched area in machine learning, with, to t…
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…
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann +1
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challe…
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…
Probabilistic Forecasting of Irregular Time Series via Conditional Flows
Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born +1
Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.…
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Sören Dittrich +2
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have…