Newsroom
Artificial Neural Networks
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FDS Seminar: Akhil Premkumar (UChicago), “An information theoretic view of machine learning”
Abstract: Diffusion models serve as a bridge between generative AI and information theory. These models have demonstrated a remarkable ability to learn high-dimensional continuous distributions, like images and video, from relatively small training datasets. They can do this because they learn the ensemble statistics of the entire dataset, allowing them to identify long wavelength correlations between…
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FDS Colloquium: Elliot Paquette (McGill), “High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws”
Abstract: Given the massive scale of modern ML models, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead, we need to understand how these models scale, allowing us to experiment with smaller problems and then apply those insights to larger-scale models. In this talk,…
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FDS Colloquium: Song Mei (Berkeley), “Revisiting neural network approximation theory in the age of generative AI”
Optional Zoom link: https://yale.zoom.us/j/97222935172 Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators,…
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Workshop Honoring Andrew Barron: Forty Years at the Interplay of Information Theory, Probability and Statistical Learning (Day 3)
The Workshop Honoring Andrew Barron: “Forty Years at the Interplay of Information Theory, Probability and Statistical Learning” will take place from Friday, April 26 to Sunday, April 28, 2024, at Yale University in New Haven, CT. Click for the main event page & registration Events include:
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Workshop Honoring Andrew Barron: Forty Years at the Interplay of Information Theory, Probability and Statistical Learning (Day 2)
The Workshop Honoring Andrew Barron: “Forty Years at the Interplay of Information Theory, Probability and Statistical Learning” will take place from Friday, April 26 to Sunday, April 28, 2024, at Yale University in New Haven, CT. Click for the main event page & registration Events include:
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Workshop Honoring Andrew Barron: Forty Years at the Interplay of Information Theory, Probability and Statistical Learning
The Workshop Honoring Andrew Barron: “Forty Years at the Interplay of Information Theory, Probability and Statistical Learning” will take place from Friday, April 26 to Sunday, April 28, 2024, at Yale University in New Haven, CT. Link to the main event page Events include: