Can Learning Theory Resist Deep Learning? - Francis Bach, INRIA
Explore the challenges of applying learning theory to deep learning in this 43-minute conference talk by Francis Bach from INRIA. Delve into recent results on global convergence of gradient descent for specific non-convex optimization problems, highlighting the difficulties and pitfalls encountered when analyzing deep learning algorithms. Examine the constant exchanges between theory and practice in machine learning, and investigate why these exchanges become more complex in the realm of deep learning. Gain insights into the intersection of statistics and computer science in modern machine learning, covering topics such as parametric supervised learning, convex optimization, stochastic gradient descent, and the theoretical analysis of deep neural networks.