6 accepted papers
Expanding neural networks during training is a promising way to augment capacity without retraining larger models from scratch. However, newly added neurons often fail to adjust to a trained network and become inactive, providing no contribution to capacity growth. We propose the Shared-Weights Exte…
Matrix Factorization (MF) has found numerous applications in Machine Learning and Data Mining, including collaborative filtering recommendation systems, dimensionality reduction, data visualization, and community detection. Motivated by the recent successes of tropical algebra and geometry in machin…
Sparse coding has been extensively used in neuroscience to model brain-like computation by drawing analogues between neurons’ firing activity and the nonzero elements of sparse vectors. Contemporary deep learning architectures have been used to model neural activity, inspired by signal processing al…
In this paper we propose a novel approach for multivariate convex regression by using as approximation model a maximum of hyperplanes, which we represent as a multivariate max-plus tropical polynomial. Our approach uses concepts from tropical geometry and finds an optimal solution for the model para…
In this work, we explore the discriminating ability of short-term signal patterns (e.g. few minutes long) with respect to the person identification task. We focus on signals recorded by simple wearable devices, such as smart watches, which can measure movements (accelerometer and gyroscope sensors)…
Weighted Finite State Transducers (WFSTs) are versatile graphical automata that can model a great number of problems, ranging from automatic speech recognition to DNA sequencing. Traditional computer science algorithms are employed when working with these automata in order to optimize their size, bu…