Yann LeCun

Yann LeCun is a person.

• See: Brown Word-Hierarchy Cluster, Deep Learning, Rational Decision-Making, Similarity Metric Learning.

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References

• Professional Homepage: http://yann.lecun.com/
• Google Scholar Author Page: http://scholar.google.com/citations?user=WLN3QrAAAAAJ

2022

• (LeCun, 2022) ⇒ Yann LeCun. (2022). “A Path Towards Autonomous Machine Intelligence.”

2016

• http://www.quora.com/What-are-the-likely-AI-advancements-in-the-next-5-to-10-years
  • Question: What are the likely AI advancements in the next 5 to 10 years?
  • Yann LeCun: There is a number of areas on which people are working hard and making promising advances:
    • deep learning combined with reasoning and planning.
    • deep model-based reinforcement learning (which involved unsupervised predictive learning)
    • recurrent neural nets augmented with differentiable memory modules (e.g. Memory Networks:
      • Memory Networks (FAIR).
      • Stack-Augmented RNN (FAIR).
      • Neural Turing Machine (DeepMind).
      • End-toEnd MemNN (FAIR/NYU).
      • and the flurry of follow-up papers.
    • generative/predictive models trained with adversarial training.
    • “differentiable programming”: this is the idea of viewing a program (or a circuit) as a graph of differentiable modules that can be trained with backprop. This points towards the possibility of not just learning to recognize patterns (as with feed-forward neural nets) but to produce algorithms (with loops, recursion, subroutines, etc). There are a few papers on this from DeepMind, FAIR and others, but it’s rather preliminary at the moment.
    • Hierarchical planning and hierarchical reinforcement learning: this is the problem of learning decompose a complex task into simpler subtasks. It seems like a requirement for intelligent systems.
    • Learning predictive models of the world in an unsupervised fashion (e.g. video prediction)
  • If significant progress is made along these directions in the next few years, we might see the emergence of considerably more intelligent AI agents for dialog systems, question-answering, adaptive robot control and planning, etc.

    A big challenge is to devise unsupervised/predictive learning methods that would allow very large-scale neural nets to “learn how the world works” by watching videos, reading textbooks, etc, without requiring explicit human-annotated data.

    This may eventually lead to machines that have learned enough about the world that we see them as having “common sense”.

    It may take 5 years, 10 years, 20 years, or more. We don’t really know.

2015

• (Zhang & LeCun, 2015) ⇒ Xiang Zhang, and Yann LeCun. (2015). “Text Understanding from Scratch.” In: arXiv:1502.01710 Journal.
• (Zhang, Zhao, & LeCun, 2015) ⇒ Xiang Zhang, Junbo Zhao, and Yann LeCun. (2015). “Character-level Convolutional Networks for Text Classification.” In: Advances in Neural Information Processing Systems, pp. 649-657.

2014

• (Sermanet et al., 2014) ⇒ Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, and Yann LeCun. (2014). “OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks.” In: International Conference on Learning Representations (ICLR 2014).

2012

• (LeCun et al., 2012) ⇒ Yann LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. “Efficient Backprop." In Neural networks: Tricks of the trade." Springer.

2005

• (Chopra et al., 2005) ⇒ Sumit Chopra, Raia Hadsell, and Yann LeCun. (2005). “Learning a Similarity Metric Discriminatively, with Application to Face Verification.” In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). ISBN:0-7695-2372-2 doi:10.1109/CVPR.2005.202

1998

• (LeCun et al., 1998) ⇒ Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. (1998). “Gradient-based Learning Applied to Document Recognition." doi:10.1109/5.726791

1993

• (Bromley et al., 1993) ⇒ Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sackinger, and Roopak Shah. (1993). “Signature Verification Using a "Siamese" Time Delay Neural Network.” In: Proceedings of NIPS Neural Information Processing Systems (NIPS). doi:10.1142/S0218001493000339

1989

• (LeCun et al., 1989) ⇒ Yann LeCun, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and Lawrence D. Jackel. (1989). “Backpropagation applied to handwritten zip code recognition." Neural computation, 1(4).

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