Fast neural solvers
Patrick Perez (Valeo AI)
| Quand ? |
Le 04/03/2019, de 11:00 à 12:00 |
|---|---|
| Où ? | Amphi. Schrödinger |
| Participants |
Patrick Perez |
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Modern artificial neural networks dominate a number of classic machine learning tasks in a wide range of application domains. What is probably less known is that they also offer new ways to attack certain optimization problems, such as inverse problems arising in physics or image processing. While a variety of powerful iterative solvers usually exist for such problems, deep learning may offer an appealing alternative: With or without supervision, neural networks can be trained to produce approximate solutions, possibly of lower quality, but orders of magnitude faster and with no need for initialization. We shall discuss different ways to design and train such fast neural solvers, with examples from computer vision and graphics.