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Yann LeCun is a French computer scientist known for his pioneering work on convolutional neural networks, optical character recognition and computer vision. He is a Silver Professor at New York University and Vice President, Chief AI Scientist at Meta. Along with Yoshua Bengio and Geoffrey Hinton, he was awarded the 2018 Turing Award for their work on deep learning, earning them the nickname of the "Godfathers of Deep Learning".

Dr. Randall Balestriero has been researching learnable signal processing since 2013, with a focus on learnable parametrized wavelets and deep wavelet transforms. His research has been used by NASA, leading to applications such as Marsquake detection. During his PhD at Rice University, Randall explored deep networks from a theoretical perspective and improved state-of-the-art methods such as batch-normalization and generative networks. Later, when joining Meta AI Research (FAIR) as a postdoc with Prof. Yann LeCun, Randall further broadened his research interests to include self-supervised learning and the biases emerging from data-augmentation and regularization, resulting in numerous publications.

Pod version: https://anchor.fm/machinelearningstre...

Note: We have another full interview with Randall, which we will release soon as part of a show focussed on Spline Theory of NNs.

TOC:

[00:00:00] LeCun interview
[00:18:25] Randall Balestriero interview (mostly on spectral SSL paper, first ref)

References:

[Randall Balestriero, Yann LeCun] Contrastive and Non-Contravention Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
https://arxiv.org/abs/2205.11508

[Randall Balestriero, Ishan Misra, Yann LeCun] A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments
https://arxiv.org/abs/2202.08325

[Bobak Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd] projUNN: efficient method for training deep networks with unitary matrices
https://arxiv.org/abs/2203.05483

[Randall Balestriero, Richard G. Baraniuk]A Spline Theory of Deep Networks
https://proceedings.mlr.press/v80/bal...

Learning in High Dimension Always Amounts to Extrapolation [Randall Balestriero, Jerome Pesenti, Yann LeCun]
https://arxiv.org/abs/2110.09485
   • #61: Prof. YANN LECUN: Interpolation,...   [MLST special edition show on extrapolation and this/spline paper]

[Mathilde Caron et al] DINO - Emerging Properties in Self-Supervised Vision Transformers
https://arxiv.org/abs/2104.14294

[Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton] A Simple Framework for Contrastive Learning of Visual Representations (SIMCLR)
https://arxiv.org/abs/2002.05709
MLST show with Simon Kornblith:    • #032- Simon Kornblith / GoogleAI - Si...  

[Yann LeCun] A Path Towards Autonomous Machine Intelligence Version
https://openreview.net/pdf?id=BZ5a1r-...

[Patrice Y. Simard, Yann A. LeCun et al]
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation
https://link.springer.com/chapter/10....

[Kaiming He et al] Masked Autoencoders Are Scalable Vision Learners
https://arxiv.org/abs/2111.06377

[Radford et al] Whisper - Robust Speech Recognition via Large-Scale Weak Supervision
https://cdn.openai.com/papers/whisper...

RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank [Quentin Garrido, Randall Balestriero, Laurent Najman, Yann Lecun]
https://arxiv.org/abs/2210.02885

[David Silver, Satinder Baveja, Doina Precup, Richard Sutton] Reward is Enough
https://www.deepmind.com/publications...