@proceedings {128, title = {A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis}, journal = {International Conference on Computer Vision}, year = {2015}, abstract = {
Unsupervised feature learning algorithms based on con- volutional formulations of independent components analysis (ICA) have been demonstrated to yield state-of-the-art results in several action recognition benchmarks. However, existing approaches do not allow for the number of latent components (features) to be automatically inferred from the data in an unsupervised manner. This is a significant dis- advantage of the state-of-the-art, as it results in consider- able burden imposed on researchers and practitioners, who must resort to tedious cross-validation procedures to obtain the optimal number of latent features. To resolve these issues, in this paper we introduce a convolutional nonpara- metric Bayesian sparse ICA architecture for overcomplete feature learning from high-dimensional data. Our method utilizes an Indian buffet process prior to facilitate inference of the appropriate number of latent features under a hybrid variational inference algorithm, scalable to massive datasets. As we show, our model can be naturally used to obtain deep unsupervised hierarchical feature extractors, by greedily stacking successive model layers, similar to existing approaches. In addition, inference for this model is completely heuristics-free; thus, it obviates the need of te- dious parameter tuning, which is a major challenge most deep learning approaches are faced with. We evaluate our method on several action recognition benchmarks, and ex- hibit its advantages over the state-of-the-art.
}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} } @proceedings {126, title = {A Non-Stationary Infinite Partially Observable Markov Decision Process}, journal = {International Conference on Artificial Neural Networks}, year = {2014}, abstract = {Partially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been successfully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space, allowing to assume that the number of visited states may grow as the agent explores its environment. These approaches rely on the assumption that the agent{\textquoteright}s environment remains stationary; however, in real-world scenarios the environment may change over time. In this work, we aim to address this inadequacy by introducing a dynamic nonparametric Bayesian POMDP model that both allows for automatic inference of the (distributional) representations of POMDP states, and for capturing non-stationarity in the modeled environments. Formulation of our method is based on imposition of a suitable dynamic hierarchical Dirichlet process (dHDP) prior over state transitions. We derive efficient algorithms for model inference and action planning and evaluate it on several benchmark tasks.}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} }