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pro vyhledávání: '"Buhmann, Joachim M"'
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
Autor:
Ovinnikov, Ivan, Buhmann, Joachim M.
Imitation learning methods are used to infer a policy in a Markov decision process from a dataset of expert demonstrations by minimizing a divergence measure between the empirical state occupancy measures of the expert and the policy. The guiding sig
Externí odkaz:
http://arxiv.org/abs/2308.09189
Autor:
Klein, Lukas, Carvalho, João B. S., El-Assady, Mennatallah, Penna, Paolo, Buhmann, Joachim M., Jaeger, Paul F.
Publikováno v:
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:689-712, 2022
Explainable AI aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to the high risk of possible fatal decisions in image-based clinical diagno
Externí odkaz:
http://arxiv.org/abs/2306.09035
With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language processing and computer vision tasks. Meanwhile, in-context learning is
Externí odkaz:
http://arxiv.org/abs/2306.08659