3:00–4:00 pm Eckhart Hall, Room 202
Understanding Deep Learning Through Optimization Geometry
How can models with more parameters than training examples generalize well, and generalize even better when we add even more parameters? In recent years, it is becoming increasingly clear that such generalization ability comes from the optimization bias, or implicit bias, of the training procedures. In this talk, I will survey our work from the past several years on highlighting the role of optimization geometry in determining such implicit bias, and understanding deep learning through it.