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Network trained to determine the parity of a four bit number Figure 5. Fitness plot for pool of networks trained to test for winning position on a tic-tac-toe board
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We describe N 2 VIS, an interactive visualization tool for feedforward neural network populations trained through evolutionary computation. N 2 VIS provides visualization of network attributes including topology, connection weights, weight volatility, and nodal activation levels for specific input values, as well as of genealogical relationships be...
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Can Hawaiians achieve well-being without sustaining their cultural identity? This research examines the very foundations of Hawaiian existence and the underlying basis and principles that shape Hawaiian identity. In particular, it looks at the historical roots of aloha ÿäina (love for the land) and the genesis of Hawaiians' spiritual and emotional...
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... The visualization method in [5] allows the user to probe into the data domain and visualize the corresponding network, errors, and uncertainty visualization, by means of Parallel coordinates [4], to help both the designer and the user of a neural network. In [7] an interactive visualization tool for feed-forward neural networks, based on tree/graph visualization, is described. Although the visualization tool is useful both as an educational device (to aid in the understanding of neural networks, search spaces, and genetic drift), and as a practical tool for solving complex problems with neural networks, the authors recognize that its main limitation is that the graphical feedforward network depiction does not scale well to networks with large numbers of nodes. ...
A challenge in ANN research is how to reduce the number of inputs to the model in high dimensional problems, so it can be
efficiently applied. The ANNs black-box operation makes not possible to explain the relationships between features and inputs.
Some numerical methods, such as sensitivity analysis, try to fight this problem. In this paper, we combine a sensitivity analysis
with a linked multi-dimensional visualization that takes advantage of user interaction, providing and efficient way to analyze
and asses both the dimension reduction results and the ANN behavior.
... These techniques are used to illustrate the idea of neural networks but are not practical due to the difficulty of showing a large network clearly. Streeter et al. [26] described an interactive visualization tool for feed-forward neural networks . Tree/graph based visualization is used in their work. ...
Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured. We have experimented with several information visualization designs aiming to open the black box to possibly uncover underlying dependencies between the input data and the output data of a neural network. In this paper, we present our designs and show that the visualizations not only help us design more efficient neural networks, but also assist us in the process of using neural networks for problem solving such as performing a classification task.
... This work forms a sub-area of KDD, called Visual Data Mining (VDM) [25,26]. Visual methods for understanding results of several classes of classifiers have been proposed, e.g., decision tree classifiers [27,28], Bayesian classifiers [29], neural networks [30], temporal data mining [31], etc. ...
Support vector machines (SVM) oer a theoretically well-founded approach to auto- mated learning of pattern classifiers. They have been proven to give highly accurate results in complex classification problems, for example, gene expression analysis. The SVM algorithm is also quite intuitive with a few inputs to vary in the fitting process and several outputs that are interesting to study. For many data mining tasks (e.g., cancer prediction) finding classifiers with good predictive accuracy is important, but understanding the classifier is equally important. By studying the classifier outputs we may be able to produce a simpler classifier, learn which vari- ables are the important discriminators between classes, and find the samples that are problematic to the classification. Visual methods for exploratory data analysis can help us to study the outputs and complement automated classification algo- rithms in data mining. We present the use of tour-based methods to plot aspects of the SVM classifier. This approach provides insights about the cluster structure in the data, the nature of boundaries between clusters, and problematic outliers. Furthermore, tours can be used to assess the variable importance. We show how visual methods can be used as a complement to cross-validation methods in order to find good SVM input parameters for a particular data set.
... These techniques are used to illustrate the idea of neural networks but are not practical due to the difficulty of showing a large network clearly. Streeter et al. [26] described an interactive visualization tool for feed-forward neural networks. Tree/graph based visualization is used in their work. ...
... Therefore it continues to be an appealing research subject for many disciplines, including mathematics, statistics, computer graphics and operations research [10,13,22,23]. The numerous visualization methods known so far are applicable to some but not all multidimensional objects encountered in science and engineering like: experimentally-obtained scattered data [3,9,28], hypergeometries [5,6,20,21,29,38,44], analytical functions [7,14,15,26,37] and inspection of the design space in optimization problems [16,24,25,36,[40][41][42], artificial neural network design [12,19,33,39], etc. The subject of the present paper is the visualization of z = F(x 1 , . . ...
Hypersurfaces of the type z=F(x1,...,xn), where F are single-valued functions of n real variables, cannot be visualized directly due to our inability to perceive dimensions higher than three. However, by projecting them down to two or three dimensions many of their properties can be revealed. In this paper a method to generate such projections is proposed, requiring successive global minimizations and maximizations of the function with respect to n-1 or n-2 variables. A number of examples are given to show the usefulness of the method, particularly for optimization problems where there is a direct interest in the minimum or maximum domains of objective functions.
Deep Learning algorithms are often used as black box type learning and they are too complex to understand. The widespread usability of Deep Learning algorithms to solve various machine learning problems demands deep and transparent understanding of the internal representation as well as decision making. Moreover, the learning models, trained on sequential data, such as audio and video data, have intricate internal reasoning process due to their complex distribution of features. Thus, a visual simulator might be helpful to trace the internal decision making mechanisms in response to adversarial input data, and it would help to debug and design appropriate deep learning models. However, interpreting the internal reasoning of deep learning model is not well studied in the literature. In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data. The proposed system allows to perceive the behavior as well as to debug the model by interactively generating adversarial audio data point. The web application of d-DeVIS is available at ddevis.herokuapp.com.
Artificial neural networks have proved successful in a broad range of applications over the last decade. However, there remain significant concerns about their interpretability. Visual representation is one way researchers are attempting to make sense of these models and their behaviour. The representation of neural networks raises questions which cross disciplinary boundaries. This chapter draws on a growing collection of interdisciplinary scholarship regarding neural networks. We present six case studies in the visual representation of neural networks and examine the particular representational challenges posed by these algorithms. Finally we summarise the ideas raised in the case studies as a set of takeaways for researchers engaging in this area.
Deep learning has recently seen rapid development and significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the innate complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such high performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, noting its short yet impactful history and summarize the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where), to thoroughly summarize deep learning visual analytics research. We conclude by highlighting research directions and open research problems. This survey helps new researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed.
Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static diagrams, or interactive toy-sized networks, which fail to illustrate the networks’ scale and complexity, and furthermore do not enable meaningful experimentation. Motivated by this observation, this paper presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real-time. The visualization is available at http:// scs. ryerson. ca/ ~aharley/ vis/ .
