Stanisław Jastrzębski

Density Invariant Detection of Osteoporosis Using Growing Neural Gas

We present a method for osteoporosis detection using graph representations obtained running a Growing Neural Gas machine learning algorithm on X–ray bone images. The GNG induced graph, being dependent on density, represents well the features which may be in part responsible for the illness. The graph connects well dense bone regions, making it possible to subdivide the whole image into regions. It is interesting to note, that these regions in bones, whose extraction might make it easier to detect the illness, correspond to some graph theoretic notions. In the paper, some invariants based on these graph theoretic notions, are proposed and if used with a machine classification method, e.g. a neural network, will make it possible to help recognize images of bones of ill persons. This graph theoretic approach is novel in this area. It helps to separate solution from the actual physical properties. The paper gives the proposed indices definitions and shows a classification based on them as input attributes.

Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time

Exponential growth in the number of compounds with experimentally verified activity towards particular target has led to the emergence of various databases gathering data on biological activity. In this study, the ligands of family A of the G Protein-Coupled Receptors that are collected in the ChEMBL database were examined, and special attention was given to serotonin receptors. Sets of compounds were examined in terms of their appearance over time, they were mapped to the chemical space of drugs deposited in DrugBank, and the emergence of structurally new clusters of compounds was indicated. In addition, a tool for detailed analysis of the obtained visualizations was prepared and made available online at http://chem.gmum.net/vischem, which enables the investigation of chemical structures while referring to particular data points depicted in the figures and changes in compounds datasets over time.

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio

We show that the dynamics and convergence properties of SGD are set by the ratio of learning rate to batch size. We observe that this ratio is a key determinant of the generalization error, which we suggest is mediated by controlling the width of the final minima found by SGD. We verify our analysis experimentally on a range of deep neural networks and datasets.

On Certain Limitations of Recursive Representation Model

There is a strong research e ort towards developing models that can achieve state-of-the-art results without sacri cing interpretability and simplicity. One of such is recently proposed Recursive Random Support Vector Machine (RSVM) model, which is composed of stacked linear models. RSVM was reported to learn deep representations outperforming many strong classiers like Deep Convolutional Neural Network. In this paper we try to analyze it both from theoretical and empirical perspective and show its important limitations. Analysis of similar model Deep Representation Extreme Learning Machine (DrELM) is also included. It is concluded that models in its current form achieves lower accuracy scores than Support Vector Machine with Radial Basis Function kernel.