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In this paper, we consider the problem of fine-tuning a discrete event simulator of distributed storage system by a neural network trained with reinforcement learning algorithms on real data. The simulator has a set of control parameters that affect its behaviour and can be tuned during the simulation. Variation of these parameters influences how realistic the simulation is. The problem of simulator tuning is equivalent to the discovery of an optimal control strategy that leads to sensible results. We investigate different optimization metrics and demonstrate the viability of the approach.
The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model - so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely its contribution to our theoretical, neural and computational understanding of visual processing. Further, the model showed how salience could be used to make predictions for both spatial and temporal distributions of fixations. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modeling, many of which tried to augment the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks, however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modeling salience, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
Brain-computer interfaces find application in a number of different areas and have the potential to be used for research as well as for practical purposes. The clinical use of BCI includes current studies on neurorehabilitation ([Frolov et al., 2013; Ang et al., 2010]), and there is the prospect of using BCI to restore movement and communication capabilities, providing alternative effective pathways to those that may be lost due to injury or illness. The processing of electrophysiological data requires analysis of high-dimensional, nonstationary, noisy signals reflecting complex underlying processes and structures. We have shown that for non-invasive neuroimaging methods such as EEG the potential improvement lies in the field of machine learning and involves designing data analysis algorithms that can model physiological and psychoemotional variability of the user. The development of such algorithms can be conducted in different ways, including the classical Bayesian paradigm as well as modern deep learning architectures. The interpretation of nonlinear decision rules implemented by multilayer structures would enable automatic and objective knowledge extraction from the neurocognitive experiments data. Despite the advantages of non-invasive neuroimaging methods, a radical increase in the bandwidth of the BCI communication channel and the use of this technology for the prosthesis control is possible only through invasive technologies. Electrocorticogram (ECoG) is the least invasive of such technologies, and in the final part of this work we demonstrate the possibility of using ECoG to decode the kinematic characteristics of the finger movement.
This workshop aims to bring together researchers, educators, practitioners who are interested in techniques as well as applications of making compact and efficient neural network representations. One main theme of the workshop discussion is to build up consensus in this rapidly developed field, and in particular, to establish close connection between researchers in Machine Learning community and engineers in industry. We believe the workshop is beneficial to both academic researchers as well as industrial practitioners.
The present article continues the investigation of the Soqotri verbal system undertaken by the Russian-Soqotri fieldwork team. The article focuses on the so-called “weak” and “geminated” roots in the basic stem. The investigation is based on the analysis of full paradigms (perfect, imperfect and jussive) of more than 170 “weak” and “geminated” Soqotri verbs.