7 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Addressing caveats of neural persistence with deep graph persistence

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    Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. Whilst this captures useful information for linear classifiers, we find that no relevant spatial structure is present in later layers of deep neural networks, making neural persistence roughly equivalent to the variance of weights. Additionally, the proposed averaging procedure across layers for deep neural networks does not consider interaction between layers. Based on our analysis, we propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers, which is equivalent to calculating neural persistence on one particular matrix. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues through standardisation. Code is available at https://github.com/ExplainableML/Deep-Graph-Persistence

    Intelligent Systems

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    This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

    Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

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    The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..

    Application of permutations to lossless compression of multispectral thematic mapper images

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    The goal of data compression is to find shorter representa- tions for any given data. In a data storage application, this is done in order to save storage space on an auxiliary device or, in the case of a communication scenario, to increase channel throughput. Because re- motely sensed data require tremendous amounts of transmission and storage space, it is essential to find good algorithms that utilize the spa- tial and spectral characteristics of these data to compress them. A new technique is presented that uses a spectral and spatial correlation to create orderly data for the compression of multispectral remote sensing data, such as those acquired by the Landsat Thematic Mapper (TM) sensor system. The method described simply compresses one of the bands using the standard Joint Photographic Expert Group (JPEG) com- pression, and then orders the next band’s data with respect to the pre- vious sorting permutation. Then, the move-to-front coding technique is used to lower the source entropy before actually encoding the data. Ow- ing to the correlation between visible bands of TM images, it was ob- served that this method yields tremendous gain on these bands (on an average 0.3 to 0.5 bits/pixel compared with lossless JPEG) and can be successfully used for multispectral images where the spectral distances between bands are close

    Adaptive sorting algorithms for evaluation of automatic zoning

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    Optical Character Recognition (OCR) involves analysis of machine-printed and hand written document images. The first step in an OCR process is to locate the text to be recognized on a page. An OCR device tries to identify the characters in these text regions and outputs the characters in ASCII. To evaluate the performance of any OCR device, the ASCII output of the OCR device is compared with the ground truth text which is entered into the computer manually; Some OCR devices provide the users with automatic zoning. The output of any automatic zoning algorithm has to be corrected manually to restore the correct reading order. This is done by elementary edit operations such as insertions, deletions and substitutions or by moving sub-strings of characters. The efficiency of an automatic zoning algorithm is measured by the cost of correcting the OCR generated text. The model for cost calculation requires movement of sub-strings in a particular fashion to ensure minimal cost. This problem has been modeled as sorting an arbitrary permutation. This thesis presents few adaptive sorting approaches which can be incorporated into the automatic zoning evaluation algorithm. These algorithms perform better than the existing algorithms used for this purpose. This thesis also presents more directions in which the problem can be pursued to achieve better performance
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