Latest Interviews

Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)

Ruslan Salakhutdinov, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

This presentation details the evolution of unsupervised learning from sparse coding and autoencoders to complex probabilistic frameworks like Restricted Boltzmann Machines, Variational Autoencoders, and Generative Adversarial Networks. It highlights how these non-probabilistic and probabilistic models overcome the scarcity of labeled data by learning hierarchical representations, with GANs notably producing sharper images than VAEs by avoiding explicit density estimation. The discussion further illustrates practical applications ranging from multimodal image-text modeling and semantic vector arithmetic to one-shot learning capabilities.