@proceedings {149, title = {Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation}, journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision}, year = {2021}, pages = {11946-11955}, abstract = {Automating sign language translation (SLT) is a challenging real-world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth. This is in stark contrast to existing end-to-end models that use gloss sequence groundtruth, either in the form of a modality that is recognized at an intermediate model stage, or in the form of a parallel output process, jointly trained with the SLT model. Our approach constitutes a Transformer network with a novel type of layers that combines: (i) local winner-takes-all (LWTA) layers with stochastic winner sampling, instead of conventional ReLU layers, (ii) stochastic weights with posterior distributions estimated via variational inference, and (iii) a weight compression technique at inference time that exploits estimated posterior variance to perform massive, almost lossless compression. We demonstrate that our approach can reach the currently best reported BLEU-4 score on the PHOENIX 2014T benchmark, but without making use of glosses for model training, and with a memory footprint reduced by more than 70\%.}, author = {Andreas Voskou and Konstantinos Panousis and Dimitrios Kosmopoulos and Dimitris Metaxas and Sotirios Chatzis} } @article {122, title = {A Latent Manifold Markovian Dynamics Gaussian Process}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, month = {01/2015}, pages = {70-83}, abstract = {
In this paper, we propose a Gaussian process model for analysis of nonlinear time series. Formulation of our model is based on the consideration that the observed data are functions of latent variables, with the associated mapping between observations and latent representations modeled through Gaussian process priors. In addition, to capture the temporal dynamics in the modeled data, we assume that subsequent latent representations depend on each other on the basis of a hidden Markov prior imposed over them. Derivation of our model is performed by marginalizing out the model parameters in closed form by using Gaussian process priors for observation mappings, and appropriate stick-breaking priors for the latent variable (Markovian) dynamics. This way, we eventually obtain a nonparametric Bayesian model for dynamical systems that accounts for uncertainty in the modeled data. We provide efficient inference algorithms for our model on the basis of a truncated variational Bayesian approximation. We demonstrate the efficacy of our approach considering a number of applications dealing with real-world data, and compare it to related state-of-the-art approaches.
}, keywords = {Gaussian process, latent manifold, Markovian dynamics, stick-breaking process, variational Bayes}, author = {Sotirios Chatzis and Dimitrios Kosmopoulos} } @proceedings {127, title = {A Nonstationary Hidden Markov ModelWith Approximately Infinitely-Long Time-Dependencies}, journal = {International Symposium on Visual Computing}, volume = {II}, year = {2014}, pages = {51-62}, edition = {LNCS, Advances in Visual Computing}, abstract = {

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation.The efficacy of our proposed model is experimentally demonstrated.

}, author = {Sotirios Chatzis and Dimitrios Kosmopoulos and George Papadourakis} }