deep neural networks

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1. 1▶05. Learning to make reward-guided decisions. Hiroyuki NakaharaDeep Learning: Theory, Algorithms, and ApplicationsNotes
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2. 2▶02. Learning in the Machine. Pierre BaldiDeep Learning: Theory, Algorithms, and ApplicationsNotes
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3. 3▶08. What can we learn from interpreting deep neural networks? Wojciech SamekDeep Learning: Theory, Algorithms, and ApplicationsNotes
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4. 4▶13. Learning from weak supervision. Masashi SugiyamaDeep Learning: Theory, Algorithms, and ApplicationsNotes
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5. 5▶01. It is time for a theory of deep learning. Tomaso PoggioDeep Learning: Theory, Algorithms, and ApplicationsNotes
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6. 6▶09. Regularized Wasserstein Distances & Minimum Kantorovich Estimators. Marco CuturiDeep Learning: Theory, Algorithms, and ApplicationsNotes
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7. 7▶03. Hunting for Cosmic Rays with Smartphones and Deep Learning. Andrey UstyuzhaninDeep Learning: Theory, Algorithms, and ApplicationsNotes
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8. 8▶14. Neural Combinatorial Optimization with Reinforcement Learning. Samy BengioDeep Learning: Theory, Algorithms, and ApplicationsNotes
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9. 9▶15. Local minima and saddle points in hierarchical structure of neural. Kenji FukumizuDeep Learning: Theory, Algorithms, and ApplicationsNotes
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10. 10▶10. Emergence of Invariance and Disentangling in Deep Representations. Alessandro AchilleDeep Learning: Theory, Algorithms, and ApplicationsNotes
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11. 11▶8 deep neural networks are our friendsLxMLS 2016Notes
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12. 12▶9 memory networks for language understandingLxMLS 2016Notes
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13. 13▶6 syntax and parsing ILxMLS 2016Notes
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14. 14▶5 learning structured predictorsLxMLS 2016Notes
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15. 15▶7 turbo parser redux from dependencies to constituentsLxMLS 2016Notes
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16. 16▶3 sequence modelsLxMLS 2016Notes
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17. 17▶2 introduction to machine kearning linear learnersLxMLS 2016Notes
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18. 18▶4 machine translation as sequence modellingLxMLS 2016Notes
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