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pro vyhledávání: '"Joachim, M"'
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations. The commonplace scarcity and heterogeneous sources of such demonstrations can lead to the absorption
Externí odkaz:
http://arxiv.org/abs/2409.08012
Autor:
Geyer, Robin C., Torcinovich, Alessandro, Carvalho, João B., Meyer, Alexander, Buhmann, Joachim M.
In unsupervised representation learning, models aim to distill essential features from high-dimensional data into lower-dimensional learned representations, guided by inductive biases. Understanding the characteristics that make a good representation
Externí odkaz:
http://arxiv.org/abs/2407.03728
Autor:
Younis, Omar G., Corinzia, Luca, Athanasiadis, Ioannis N., Krause, Andreas, Buhmann, Joachim M., Turchetta, Matteo
Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision
Externí odkaz:
http://arxiv.org/abs/2406.03932
The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of v
Externí odkaz:
http://arxiv.org/abs/2405.19614
With the emergence of large-scale models trained on diverse datasets, in-context learning has emerged as a promising paradigm for multitasking, notably in natural language processing and image processing. However, its application in 3D point cloud ta
Externí odkaz:
http://arxiv.org/abs/2404.12352
Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance. Traditional domain generalization aims to learn domain-invariant features, but the federated nature of model averaging often limits
Externí odkaz:
http://arxiv.org/abs/2402.06974
Autor:
Li, Xia, Bellotti, Renato, Bachtiary, Barbara, Hrbacek, Jan, Weber, Damien C., Lomax, Antony J., Buhmann, Joachim M., Zhang, Ye
Background: In MR-guided proton therapy planning, aligning MR and CT images is key for MR-based CT synthesis, especially in mobile regions like the head-and-neck. Misalignments here can lead to less accurate synthetic CT (sCT) images, impacting treat
Externí odkaz:
http://arxiv.org/abs/2401.12878
Anomaly detection (AD) is the machine learning task of identifying highly discrepant abnormal samples by solely relying on the consistency of the normal training samples. Under the constraints of a distribution shift, the assumption that training sam
Externí odkaz:
http://arxiv.org/abs/2312.14329