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• Deep Learning: Theory, Algorithms, and Applications

  10 posts · theirs

Lately

• 05. Learning to make reward-guided decisions. Hiroyuki Nakahara

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-14

• 02. Learning in the Machine. Pierre Baldi

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 08. What can we learn from interpreting deep neural networks? Wojciech Samek

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 13. Learning from weak supervision. Masashi Sugiyama

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 01. It is time for a theory of deep learning. Tomaso Poggio

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 09. Regularized Wasserstein Distances & Minimum Kantorovich Estimators. Marco Cuturi

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 03. Hunting for Cosmic Rays with Smartphones and Deep Learning. Andrey Ustyuzhanin

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 14. Neural Combinatorial Optimization with Reinforcement Learning. Samy Bengio

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 15. Local minima and saddle points in hierarchical structure of neural. Kenji Fukumizu

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

• 10. Emergence of Invariance and Disentangling in Deep Representations. Alessandro Achille

  Deep Learning: Theory, Algorithms, and Applications · 2017-08-03

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