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The idea of reusing information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency reinforcement learning agents. In this work, we…
Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to…
Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases.…
Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions…
We study the effect of modifications to General Relativity on large scale weak lensing observables. In particular, we consider three modified gravity scenarios: f(R) gravity, the DGP model, and TeVeS theory. Weak lensing is sensitive to the…
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this…
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work…
Chameleon and similar (symmetron and dilation) theories of gravity can exhibit new and interesting features on cosmological scales whilst screening the modifications on small scales thereby satisfying solar system tests of general…
The theoretical basis of dark energy remains unknown and could signify a need to modify the laws of gravity on cosmological scales. In this study we investigate how the clustering and motions of galaxies can be used as probes of modified…
We introduce a multidimensional, neural-network approach to reveal and measure urban segregation phenomena, based on the Self-Organizing Map algorithm (SOM). The multidimensionality of SOM allows one to apprehend a large number of variables…
Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new…
This paper takes an information visualization perspective to visual representations in the general SOM paradigm. This involves viewing SOM-based visualizations through the eyes of Bertin's and Tufte's theories on data graphics. The regular…
We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large numbers of galaxy spectral templates into a corrresponding collection of "fuzzy archetypes"…
We consider the impact of key astrophysical and measurement systematics on constraints on dark energy and modifications to gravity on cosmic scales. We focus on upcoming photometric "Stage III" and "Stage IV" large scale structure surveys…
The acceleration of the expansion of the Universe has led to the construction of Dark Energy models where a light scalar field may have a range reaching up to cosmological scales. Screening mechanisms allow these models to evade the tight…
The upcoming galaxy large-scale surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will generate photometry for billions of galaxies. The interpretation of large-scale weak lensing maps, as well as the…
Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss…
We consider the dilaton gravity models derived by reductions of generalized theories of gravity and study one-dimensional dynamical systems simultaneously describing cosmological and static states in any gauge. Our approach is fully…
Modified gravity theories often contain a scalar field of gravitational strength which interacts with matter. We examine constraints on the range and the coupling strength of a scalar gravitational degree of freedom using a subset of…
Parameter prediction is essential for many applications, facilitating insightful interpretation and decision-making. However, in many real life domains, such as power systems, medicine, and engineering, it can be very expensive to acquire…