1 Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 Advanced Machine Learning Lecture 12 Neural Networks 24.11.2016 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de/ leibe@vision.rwth-aachen.de Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 Talk Announcement • Yann LeCun (NYU & FaceBook AI) 28.11. 15:00-16:30h, SuperC 6 th floor (Ford Saal) The rapid progress of AI in the last few years are largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast GPUs. We now have systems that can recognize images with an accuracy that rivals that of humans. This will lead to revolutions in several domains such as autonomous transportation and medical image analysis. But all of these systems currently use supervised learning in which the machine is trained with inputs labeled by humans. The challenge of the next several years is to let machines learn from raw, unlabeled data, such as video or text. This is known as predictive (or unsupervised) learning. Intelligent systems today do not possess "common sense", which humans and animals acquire by observing the world, by acting in it, and by understanding the physical constraints of it. I will argue that the ability of machines to learn predictive models of the world is a key component of that will enable significant progress in AI. The main technical difficulty is that the world is only partially predictable. A general formulation of unsupervised learning that deals with partial predictability will be presented. The formulation connects many well-known approaches to unsupervised learning, as well as new and exciting ones such as adversarial training. • No lecture next Monday - go see the talk! 2 B. Leibe Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 This Lecture: Advanced Machine Learning • Regression Approaches  Linear Regression  Regularization (Ridge, Lasso)  Kernels (Kernel Ridge Regression)  Gaussian Processes • Approximate Inference  Sampling Approaches  MCMC • Deep Learning  Linear Discriminants  Neural Networks  Backpropagation  CNNs, RNNs, ResNets, etc. B. Leibe Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 Recap: Generalized Linear Discriminants • Extension with non-linear basis functions  Transform vector x with M nonlinear basis functions Á j (x):  Basis functions Á j (x) allow non-linear decision boundaries.  Activation function g( ¢ ) bounds the influence of outliers.  Disadvantage: minimization no longer in closed form. • Notation 4 B. Leibe with Á 0 (x)=1 Slide adapted from Bernt Schiele Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 Recap: Gradient Descent • Iterative minimization  Start with an initial guess for the parameter values .  Move towards a (local) minimum by following the gradient. • Basic strategies  “Batch learning”  “Sequential updating” where 5 B. Leibe w (¿ +1) kj = w (¿ ) kj ¡ ´ @E(w) @w kj ¯ ¯ ¯ ¯ w (¿) w (0) kj w (¿ +1) kj = w (¿ ) kj ¡ ´ @E n (w) @w kj ¯ ¯ ¯ ¯ w (¿) E(w)= N X n=1 E n (w) Perceptual and Sensory Augmented Computing Advanced Machine Learning Winter’16 Recap: Gradient Descent • Example: Quadratic error function • Sequential updating leads to delta rule (=LMS rule)  where  Simply feed back the input data point, weighted by the classification error. 6 B. Leibe w (¿ +1) kj = w (¿ ) kj ¡ ´ (y k (x n ; w) ¡ t kn ) Á j (x n ) = w (¿ ) kj ¡ ´± kn Á j (x n ) ± kn = y k (x n ; w) ¡ t kn Slide adapted from Bernt Schiele E(w)= N X n=1 (y(x n ; w) ¡ t n ) 2