37 Avsnitt

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

    Publicerades: 2022-02-28

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

    Publicerades: 2021-12-21

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

    Publicerades: 2021-10-15

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

    Publicerades: 2021-09-24

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

    Publicerades: 2021-09-10

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

    Publicerades: 2021-06-18

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

    Publicerades: 2021-05-20

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

    Publicerades: 2021-05-12

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

    Publicerades: 2021-04-02

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

    Publicerades: 2021-03-27

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

    Publicerades: 2021-03-18

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

    Publicerades: 2021-03-05

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

    Publicerades: 2021-02-24

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

    Publicerades: 2021-02-17

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

    Publicerades: 2021-02-01

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

    Publicerades: 2021-01-07

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

    Publicerades: 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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