A Machine Learning Perspective on Predictive Coding with PAQ Byron Knoll & Nando de Freitas University of British Columbia Vancouver, Canada fknoll,
[email protected] August 17, 2011 Abstract PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a starting point for discussions that will increase our understanding, lead to improvements to PAQ8, and facilitate a transfer of knowledge from PAQ8 to other machine learning methods, such a recurrent neural networks and stochastic memoizers. Finally, the report presents a broad range of new applications of PAQ to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and compression using features from the field of deep learning. 1 Introduction Detecting temporal patterns and predicting into the future is a fundamental problem in machine learning. It has gained great interest recently in the areas of nonparametric Bayesian statistics (Wood et al., 2009) and deep learning (Sutskever et al., 2011), with applications to several domains including language modeling and unsupervised learning of audio and video sequences. Some re- searchers have argued that sequence prediction is key to understanding human intelligence (Hawkins and Blakeslee, 2005). The close connections between sequence prediction and data compression are perhaps under- arXiv:1108.3298v1 [cs.LG] 16 Aug 2011 appreciated within the machine learning community.