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Deep Learning: Theory, Algorithms, and Applications

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

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02. Learning in the Machine. Pierre Baldi

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

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13. Learning from weak supervision. Masashi Sugiyama

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

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

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

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14. Neural Combinatorial Optimization with Reinforcement Learning. Samy Bengio

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

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

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