Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning

Expectation-Maximization for Learning Determinantal Point Processes

A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we would like to learn the entries of its kernel matrix by maximizing the log-likelihood of the available data. However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjec...

Using Expectation-Maximization for Reinforcement Learning

We discuss Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-...

Greedy Deep Dictionary Learning

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like s...

Learning Deep State Representations With Convolutional Autoencoders

Advances in artificial intelligence algorithms and techniques are quickly allowing us to create artificial agents that interact with the real world. However, these agents need to maintain a carefully constructed abstract representation of the world around them [9]. Recent research in deep reinforcement learning attempts to overcome this challenge. Mnih et al. [24] at DeepMind and Levine et al. ...

Expectation Maximization