3rd Edition of Mathematics for and by Large Language Models

Liste des Contributions

1. TBA

Quentin Berthet (Google DeepMind)

28/05/2026 09:30

TBA

2. The Expressive Power of Large Language Models

Gabriel Peyré (CNRS, DMA, École Normale Supérieure)

28/05/2026 10:30

Large language models process vast sequences of input tokens by alternating between classical multi-layer perceptron layers and self-attention mechanisms. While the approximation capabilities of perceptrons are relatively well understood, those of attention mechanisms remain less explored. In this talk, I will compare the proof techniques and approximation results associated with these two...

3. ReLU and Softplus Neural Nets as Zero-Sum, Turn-Based, Stopping Games

Yiannis Vlassopoulos (Athena Research Center & IHES)

28/05/2026 12:00

Neural networks are for the most part treated as black boxes.
In an effort to understand the mathematical structure that underlies them we will explain how ReLU neural nets can be interpreted as zero-sum, turn-based, stopping games.

The game runs in the opposite direction to the net. The input to the net is the terminal reward of the game, the output of every neuron turns out to be equal...

4. TBA

Edward Lockhart (Google DeepMind)

28/05/2026 14:00

TBA