Lawrence K. Saul

TL;DR: This work establishes global convergence of the dynamics by providing a Lyapunov function and shows that the dynamics generate the signals required for unsupervised learning.

TL;DR: In this article, factor analysis is used to model acoustic correlation in automatic speech recognition by introducing a small number of parameters to model the covariance structure of a speech signal, which are estimated by an Expectation Maximization (EM) technique that can be embedded in the training procedures for the HMMs, and then further adjusted using Minimum Classification Error (MCE) training, which demonstrates better discrimination and produces more accurate recognition models.

TL;DR: This work develops low-rank approximations of the covariance structure of the weights of second-order algorithms for linear classification and shows improvements over diagonal covariance matrices for both low and high-dimensional data.

TL;DR: A robust system for numeric recognition is described and algorithms for feature extraction, acoustic and language modeling, discriminative training, utterance verification and numeric understanding and validation are presented.

TL;DR: Proverbot9001 as mentioned in this paper is a proof search system using machine learning techniques to produce proofs of software correctness in interactive theorem provers, which can effectively automate what were previously manual proofs, automatically producing proofs for 28% of theorem statements in our test dataset, when combined with solver-based tooling.