Sökning: "Equivariant Neural Networks"
Hittade 5 avhandlingar innehållade orden Equivariant Neural Networks.
1. G-equivariant convolutional neural networks
Sammanfattning : Over the past decade, deep learning has revolutionized industry and academic research. Neural networks have been used to solve a multitude of previously unsolved problems and to significantly improve the state-of-the-art on other tasks, in some cases reaching superhuman levels of performance. LÄS MER
2. Mathematical Foundations of Equivariant Neural Networks
Sammanfattning : Deep learning has revolutionized industry and academic research. Over the past decade, neural networks have been used to solve a multitude of previously unsolved problems and to significantly improve the state of the art on other tasks. However, training a neural network typically requires large amounts of data and computational resources. LÄS MER
3. Equivariant Neural Networks for Biomedical Image Analysis
Sammanfattning : While artificial intelligence and deep learning have revolutionized many fields in the last decade, one of the key drivers has been access to data. This is especially true in biomedical image analysis where expert annotated data is hard to come by. LÄS MER
4. Representation Learning and Information Fusion : Applications in Biomedical Image Processing
Sammanfattning : In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. LÄS MER
5. On Symmetries and Metrics in Geometric Inference
Sammanfattning : Spaces of data naturally carry intrinsic geometry. Statistics and machine learning can leverage on this rich structure in order to achieve efficiency and semantic generalization. Extracting geometry from data is therefore a fundamental challenge which by itself defines a statistical, computational and unsupervised learning problem. LÄS MER