Point Processes as Primitives of Computation

Modern machine learning methods have proved remarkably successful at inferring statistical structure from data, something that any intelligent system must be able to perform. However, there is a disconnect between how our algorithms are represented in computer hardware and what we understand about the hardware of natural neural systems. In particular, we are still trying to understand how action potentials (neural spikes) can be used to implement adaptive computation. An ongoing project in the HIPS group is to try to formalize such computation in terms of powerful statistical objects called point processes.
Discovering Latent Network Structure in Point Process Data. Linderman, SW, Adams RP.  Thirty-First International Conference on Machine Learning (ICML). 2014.   { arXiv:1402.0914 [stat.ML] | PDF }
Learning the Parameters of Determinantal Point Process Kernels. Affandi, RH, Fox EB, Adams RP, Taskar B.  Thirty-First International Conference on Machine Learning (ICML). 2014.   { arXiv:1402.4862 [stat.ML] | PDF }
Priors for Diversity in Generative Latent Variable Models. Zou, JY, Adams RP.  Advances in Neural Information Processing Systems 25. 2012.   { PDF }