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#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality

2022-02-22 · 93 min · episode 63 · 9 entities

episode page audio

Asserted relationships

  • → discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology
  • → references Mlst website
    0.38
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": http://wandb.me/MLST
  • → references Bengio Yoshua person
    0.38
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": https://mila.quebec/en/person/bengio-yoshua

Entities found in this episode

companys 2

  • mentioned Technology company
    0.50
    evidence rules-v4
    Feed category: Technology
  • discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology

websites 2

  • mentioned Mlst website
    0.45
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": http://wandb.me/MLST
  • references Mlst website
    0.38
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": http://wandb.me/MLST

persons 2

  • mentioned Bengio Yoshua person
    0.45
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": https://mila.quebec/en/person/bengio-yoshua
  • references Bengio Yoshua person
    0.38
    evidence rules-v4
    Link in episode "#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality": https://mila.quebec/en/person/bengio-yoshua

concepts 2

  • mentioned YOSHUA concept
    0.35
    evidence rules-v4
    YOSHUA
  • mentioned BENGIO concept
    0.35
    evidence rules-v4
    BENGIO

podcasts 1

Episode description as stored
We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Patreon: https://www.patreon.com/mlst For Yoshua Bengio, GFlowNets are the most exciting thing on the horizon of Machine Learning today. He believes they can solve previously intractable problems and hold the key to unlocking machine abstract reasoning itself. This discussion explores the promise of GFlowNets and the personal journey Prof. Bengio traveled to reach them. Panel: Dr. Tim Scarfe Dr. Keith Duggar Dr. Yannic Kilcher Our special thanks to: - Alexander Mattick (Zickzack) References: Yoshua Bengio @ MILA (https://mila.quebec/en/person/bengio-yoshua/) GFlowNet Foundations (https://arxiv.org/pdf/2111.09266.pdf) Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (https://arxiv.org/pdf/2106.04399.pdf) Interpolation Consistency Training for Semi-Supervised Learning (https://arxiv.org/pdf/1903.03825.pdf) Towards Causal Representation Learning (https://arxiv.org/pdf/2102.11107.pdf) Causal inference using invariant prediction: identification and confidence intervals (https://arxiv.org/pdf/1501.01332.pdf)