Monday, December 19, 2011

David Pfau: Dec. 20th

David will be giving a fly-by view of a number of cool papers from NIPS.

First is Empirical Models of Spiking in Neural Populations by Macke, Büsing, Cunningham, Yu, Shenoy and Mahani, where they evaluate the relative merits of GLMs with pairwise coupling and state space models on multielectrode recording in motor cortex.

Next, Quasi-Newton Methods for Markov Chain Monte Carlo by Zhang and Sutton looks at how to use approximate second-order methods like L-BFGS for MCMC while still preserving detailed balance.

Then, Demixed Principal Component Analysis is an extension of PCA which demixes the dependence of different latent dimensions on different observed parameters, and is used to analyze neural data from PFC

Finally, Learning to Learn with Compound Hierarchical-Deep Models, which combines a deep neural network for learning visual features with a hierarchical nonparametric Bayesian model for learning object categories to make one cool-looking demo.

Wednesday, December 7, 2011

Previous Group Meetings (for archival purposes)

Universal MAP Estimation in Compressed Sensing, by Baron and Duarte
Quantifying Statistical Interdependence by Message Passing on Graphs, by Dauwels, Vialatte, Weber and Chichocki. Part I and Part II

Ari Pakman: Dec. 13th

"Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models" by Gerhard and Gerstner (NIPS 2010).

The abstract reads:

"Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-fit using methods from point-process theory, e.g. the time-rescaling theorem. However, high neural firing rates or coarse discretization lead to a breakdown of the assumptions necessary for this connection. Here, we show how goodness-of-fit tests from point-process theory can still be applied to GLMs by constructing equivalent surrogate point processes out of time-series observations. Furthermore, two additional tests based on thinning and complementing point processes are introduced. They augment the instruments available for checking model adequacy of point processes as well as discretized models."