4 papers
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme +1
Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While re…
Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
The primary paradigm in Neural Combinatorial Optimization (NCO) are construction methods, where a neural network is trained to sequentially add one solution component at a time unt…
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…
On Distributional Dependent Performance of Classical and Neural Routing Solvers
Daniela Thyssens, Tim Dernedde, Wilson Sentanoe +1
Neural Combinatorial Optimization aims to learn to solve a class of combinatorial problems through data-driven methods and notably through employing neural networks by learning the…