View Seminar Video Abstract Markov switching processes, such as hidden Markov models (HMMs) and switching linear dynamical systems (SLDSs), are often used to describe rich classes of dynamical phenomena. They describe complex temporal behavior via[…]
View Seminar Video Abstract We describe our efforts to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more facts from[…]
Abstract A Minimum Bayes-Risk (MBR) decoder seeks the hypothesis with the least expected loss function for a given task. In the field of machine translation, the technique was originally developed for rescoring k-best lists of[…]
View Seminar Video Abstract Probabilistic topic modeling provides a suite of tools for analyzing large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to[…]
View Seminar Video Abstract Human infants learn spontaneously and effortlessly the language(s) spoken in their environments, despite the extraordinary complexity of the task. In the past 30 years, tremendous progress has been made regarding the[…]
Abstract Humans possess a remarkable ability to attend to a single speaker’s voice in a multi-talker background. How the auditory system manages to extract intelligible speech under such acoustically complex and adverse listening conditions is[…]
View Seminar Video Abstract As statistical methods came to dominate computer vision, speech recognition and machine translation there was a tendency toward shallow models. The late Fred Jelinek is famously quoted as saying that every[…]
