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Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper,...
The Nyström method is a matrix approximation technique that has shown great promise in speeding up spectral clustering. However, when the input matrix is sparse, we show that the traditional Nyström method requires a prohibitively large number of samples to obtain a good approximation. We propose a novel sampling approach to select the landmark points used to compute the Nyström approximation. We...
Regression-Discontinuity Design is a non-experimental method to estimate the impacts of social welfare programs in situations where the treatment assignment is determined by whether an observed variable (running variable) is above or below a known cutoff point. The main idea behind RDD is that individuals whose running variable is just above or just below the cutoff are similar, and so, any differences...
In the last few years, recurrent neural networks (RNNs) have become the Swiss Army knife of large-scale sequence processing. Problems involving long and complex data streams, such as speech recognition, machine translation and reinforcement learning from raw video, are now routinely tackled with RNNs. This talk takes a look at some of the new architectures, applications and training strategies currently...
There is very little practicable significance to prove the equivalency between a pseudo-inverse linear discriminant (PILD) with the desired outputs in reverse proportion to the number of within-class samples and a Fisher linear discriminant (FLD) with the totally projected mean thresholds which are disadvantageous to improve the overall classification accuracy. Even if so, several examples have borne...
Emotions are related to many different parts of our lives: from the perception of the environment around us to different learning processes and natural communication. Therefore, it is very hard to achieve an automatic emotion recognition system which is adaptable enough to be used in real-world scenarios. This paper proposes the use of a growing and self-organizing affective memory architecture to...
Many studies emphasize the importance of infant-directed speech: stronger articulated, higher-quality speech helps infants to better distinguish different speech sounds. This effect has been widely investigated in terms of the infant's perceptual capabilities, but few studies examined whether infant-directed speech has an effect on articulatory learning. In earlier studies, we developed a model that...
Two different configurations of Liquid State Machine (LSM), a special type of Reservoir Computing with internal nodes modelled as spiking neurons, implementing multiple columns (Modular and Monolithic approaches) are tested against the decimation of neurons, connections and entire columns in order to verify which one can better withstand the damage. Based on the neurorobotics outlook, this work is...
What makes a computational neuronal model ‘large scale’? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across co-ordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explore...
Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major step in developing Brain Computer Interfaces (BCIs). Features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the electroencephalography (EEG) training signals for each class...
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