Generative Model That Won 2024 Nobel Prize

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My name is Artem, I'm a neuroscience PhD student at Harvard University.
🌎 Website and Social links: https://kirsanov.ai/
📥 "Receptive Field" neuro-newsletter: https://artemkirsanov.substack.com/
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In this video we explore Boltzmann Machines – one of the first generative models that learns probability distribution of data, leveraging stochastic rules and latent representations.

🕒 OUTLINE:
00:00 Introduction
01:56 Goal of Boltzmann Machines
05:26 Boltzmann Distribution
13:29 Stochastic Update Rule
17:39 Contrastive Hebbian Rule
25:41 Hidden Units
28:25 Restricted Boltzmann Machines
29:38 Conclusion & Outro

📚 FURTHER READING & REFERENCES:
1. Ackley, D., Hinton, G. & Sejnowski, T. A learning algorithm for boltzmann machines. Cognitive Science 9, 147–169 (1985).
2. Downing, K. L. Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks. (The MIT Press, Cambridge, Massachusetts, 2023).
3. Hinton, G. E. & Salakhutdinov, R. R. Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507 (2006).
4. Hinton, G. E. A Practical Guide to Training Restricted Boltzmann Machines. in Neural Networks: Tricks of the Trade (eds. Montavon, G., Orr, G. B. & Müller, K.-R.) vol. 7700 599–619 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2012).

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Special thanks to Crimson Ghoul for providing English subtitles!
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Disclaimer: This channel is my personal project. The views and content expressed here are my own and are separate from my research role at Harvard University.

#MachineLearning #NobelPrize #ArtificialIntelligence


Description remastered: February 2026. Links & Bio updated; original context preserved.