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Science ; AI girls and guys AND Machine learning

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22 Documents

Lenka Zdeborova sur X : "Emergence in LLMs is a mystery. Emergence in physics is linked to phase transitions. We identify a phase transition between semantic and positional learning in a toy model of dot-product attention"

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2024-02-07 About

Stéphane d'Ascoli sur X : "Think Transfomers are terrible at logical reasoning? Think again. Transformers trained with Boolean inputs and symbolic outputs..."

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2023-09-26 About

Lenka Zdeborova sur Twitter : "Let me post the concluding slide from my talk today at the ICLR workshop on Mathematical and Empirical Understanding of Foundation Models"

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2023-05-04 About

ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation

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2023-05-04 About

The Physics of Algorithms, by Prof. Lenka Zdeborová - YouTube

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2022-12-03 About

Unveiling Transformers with LEGO - YouTube

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2022-06-30 About

Lenka Zdeborova sur Twitter : "Amazing lecture of @SebastienBubeck giving intuition on transformers and how to approach them theoretically..."

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2022-06-30 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "my two cents on why NLP as a field is focusing on the ML-ish / algorithmic / leaderboard-ish aspects (incl., now, LLMs) and not on the underlying language phenomena: it is just so much easier, on so many levels."

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2021-08-30 About

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation | Frontiers in Computational Neuroscience

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2021-03-19 About

[2003.03384] AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

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2020-03-17 About

[1912.03927] Large deviations for the perceptron model and consequences for active learning

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2019-12-11 About

[1912.01412] Deep Learning for Symbolic Mathematics

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2019-12-09 About

[1807.00082] Amanuensis: The Programmer's Apprentice

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**The use of natural language to facilitate communication
between the expert programmer and apprentice AI system.**

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 About

France is AI 2018: Lenka Zdeborova - Statistical physics modelling of machine learning - YouTube

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2019-08-15 About

[1907.03950] Learning by Abstraction: The Neural State Machine

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2019-07-10 About

Towards bridging the gap between deep learning and brains

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2018-10-23 About

Cracking big data with statistical physics

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2018-10-18 About

From Deep Learning of Disentangled Representations to Higher-level Cognition - YouTube

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2018-09-28 About

Stanford Seminar - "Can the brain do back-propagation?" - Geoffrey Hinton

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2018-01-22 About

[1709.08568] The Consciousness Prior

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2017-09-29 About

How Mimicking Brain Function is Revolutionising NLP - Dataconomy

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2017-04-28 About

Jeff Hawkins: How brain science will change computing - CornellCast

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2013-05-25 About