15 citations · 33 across the 8 of their papers we have counts for
9 papers
Recurrent State Encoders for Efficient Neural Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
The primary paradigm in Neural Combinatorial Optimization (NCO) consists of construction methods, where a neural network is trained to sequentially add one solution component at a…
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…
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…
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Daniela Thyssens, Tim Dernedde, Jonas K. Falkner +1
Neural Combinatorial Optimization has been researched actively in the last eight years. Even though many of the proposed Machine Learning based approaches are compared on the same…
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
Christian Löwens, Daniela Thyssens, Emma Andersson +2
Nowadays, mobile devices enable constant tracking of the user's position and location trajectories can be used to infer personal points of interest (POIs) like homes, workplaces, o…
Learning to Control Local Search for Combinatorial Optimization
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir +1
Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually…
Large Neighborhood Search based on Neural Construction Heuristics
Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme
We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem wi…
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
Daniela Thyssens, Jonas Falkner, Lars Schmidt-Thieme
Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations research solvers and heu…
Do We Really Need Deep Learning Models for Time Series Forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed +2
Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and…