How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

Distribution theory for hierarchical processes Page 1 The Annals of Statistics 2019, Vol. 47, No.
1, 67–92 https://doi.org/10.1214/17-AOS1678 © Institute of Mathematical Statistics, 2019 …

We present a novel approach for event detection in video by temporal sequence modeling.
Exploiting temporal information has lain at the core of many approaches for video analysis …

Learning from streams of evolving and unbounded data is an important problem, for
example in visual surveillance or internet scale data. For such large and evolving real-world …

A fundamental characteristic of natural language is the high rate at which speakers produce
novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a …

Recent sequential pattern mining methods have used the minimum description length (MDL)
principle to define an encoding scheme which describes an algorithm for mining the most …

Structural locality is a ubiquitous feature of real-world datasets, wherein data points are
organized into local hierarchies. Some examples include topical clusters in text or project …

Neural language models are a critical component of state-of-the-art systems for machine
translation, summarization, audio transcription, and other tasks. These language models are …