Takip et
Michael Kölle
Michael Kölle
Diğer adlarMichael Koelle
LMU Munich
ifi.lmu.de üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Attention-based recurrence for multi-agent reinforcement learning under stochastic partial observability
T Phan, F Ritz, P Altmann, M Zorn, J Nüßlein, M Kölle, T Gabor, ...
International Conference on Machine Learning, 27840-27853, 2023
122023
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
J Stein, F Chamanian, M Zorn, J Nüßlein, S Zielinski, M Kölle, ...
Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023
102023
Quantum Denoising Diffusion Models
M Kölle, G Stenzel, J Stein, S Zielinski, B Ommer, C Linnhoff-Popien
arXiv preprint arXiv:2401.07049, 2024
82024
Exploring unsupervised anomaly detection with quantum boltzmann machines in fraud detection
J Stein, D Schuman, M Benkard, T Holger, W Sajko, M Kölle, J Nüßlein, ...
arXiv preprint arXiv:2306.04998, 2023
62023
Learning to Participate through Trading of Reward Shares
M Kölle, T Matheis, P Altmann, K Schmid
arXiv preprint arXiv:2301.07416, 2023
62023
Improving convergence for quantum variational classifiers using weight re-mapping
M Kölle, A Giovagnoli, J Stein, MB Mansky, J Hager, C Linnhoff-Popien
arXiv preprint arXiv:2212.14807, 2022
62022
A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
M Kölle, T Schubert, P Altmann, M Zorn, J Stein, C Linnhoff-Popien
arXiv preprint arXiv:2401.07054, 2024
52024
Quantum Advantage Actor-Critic for Reinforcement Learning
M Kölle, M Hgog, F Ritz, P Altmann, M Zorn, J Stein, C Linnhoff-Popien
arXiv preprint arXiv:2401.07043, 2024
52024
Challenges for reinforcement learning in quantum computing
P Altmann, A Bärligea, J Stein, M Kölle, T Gabor, T Phan, ...
arXiv preprint arXiv:2312.11337, 2023
42023
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
M Kölle, F Topp, T Phan, P Altmann, J Nüßlein, C Linnhoff-Popien
arXiv preprint arXiv:2311.05546, 2023
42023
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
M Kölle, T Witter, T Rohe, G Stenzel, P Altmann, T Gabor
arXiv preprint arXiv:2405.12354, 2024
32024
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
M Kölle, A Ahouzi, P Debus, R Müller, D Schuman, C Linnhoff-Popien
arXiv preprint arXiv:2312.09174, 2023
32023
Constructing organism networks from collaborative self-replicators
S Illium, M Zorn, C Lenta, M Kölle, C Linnhoff-Popien, T Gabor
2022 IEEE Symposium Series on Computational Intelligence (SSCI), 1268-1275, 2022
32022
SEGym: optimizing large language model assisted software engineering agents with reinforcement learning
G Stenzel, K Schmid, M Kölle, P Altmann, M Lingsch-Rosenfeld, M Zorn, ...
International Conference on Bridging the Gap between AI and Reality, 107-124, 2024
22024
Towards Federated Learning on the Quantum Internet
L Sünkel, M Kölle, T Rohe, T Gabor
International Conference on Computational Science, 330-344, 2024
22024
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
R Müller, H Turalic, T Phan, M Kölle, J Nüßlein, C Linnhoff-Popien
arXiv preprint arXiv:2401.03504, 2024
22024
Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
J Stein, N Roshani, M Zorn, P Altmann, M Kölle, C Linnhoff-Popien
arXiv preprint arXiv:2312.05552, 2023
22023
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
M Kölle, J Maurer, P Altmann, L Sünkel, J Stein, C Linnhoff-Popien
arXiv preprint arXiv:2311.05559, 2023
22023
Introducing Reducing-Width-QNNs, an AI-inspired Ansatz design pattern
J Stein, T Rohe, F Nappi, J Hager, D Bucher, M Zorn, M Kölle, ...
arXiv preprint arXiv:2306.05047, 2023
22023
Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty.
T Phan, F Ritz, J Nüßlein, M Kölle, T Gabor, C Linnhoff-Popien
AAMAS, 2839-2841, 2023
22023
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