Learning from complex data like graphs and multi-dimensional points
Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning.
This workshop identifies the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning.
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Learning from complex data like graphs and multi-dimensional points
Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning.
This workshop identifies the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning.
No items found.
No items found.
Previous Article
Next Article
Learning from complex data like graphs and multi-dimensional points
Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning.
This workshop identifies the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning.
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