37 Epizód

  1. Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology

    Közzétéve: 2022. 02. 28.

  2. Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity

    Közzétéve: 2021. 12. 21.

  3. Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

    Közzétéve: 2021. 10. 15.

  4. Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

    Közzétéve: 2021. 09. 24.

  5. Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

    Közzétéve: 2021. 09. 10.

  6. Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

    Közzétéve: 2021. 06. 18.

  7. Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

    Közzétéve: 2021. 05. 20.

  8. Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

    Közzétéve: 2021. 05. 12.

  9. Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

    Közzétéve: 2021. 04. 02.

  10. Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations

    Közzétéve: 2021. 03. 27.

  11. Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models

    Közzétéve: 2021. 03. 18.

  12. Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions

    Közzétéve: 2021. 03. 05.

  13. Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding

    Közzétéve: 2021. 02. 24.

  14. Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning

    Közzétéve: 2021. 02. 17.

  15. Episode 03: Cinjon Resnick, NYU, on activity and scene understanding

    Közzétéve: 2021. 02. 01.

  16. Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process

    Közzétéve: 2021. 01. 07.

  17. Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems

    Közzétéve: 2020. 12. 15.

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Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

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