Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton

This seminar examines how deep-learning models based on machine-translation techniques can solve symbolic integration and ordinary differential equations. It covers expression representation, synthetic data generation, model evaluation, generalization, and equivalent solutions.

Syllabus

Introduction.
Deep learning for symbolic mathematics.
Starting point.
Basic intuition.
The plan.
From expressions to trees.
Generating data.
Symbolic integration (forward approach).
Symbolic integration (backward approach).
Symbolic integration (integration by parts).
Ordinary Differential Equations (order 1).
Ordinary Differential Equations (ODE) - orde.
Ordinary Differential Equations (order 2).
Datasets.
The model.
Evaluation.
Comparison with Mathematica.
Integration-generalization issues.
Generalization - looking bad.
Generalization - looking better.
Generalization - looking forward.
Generalization - a fun fact.
Inside the beam - Equivalent solutions.
References.