In situ TensorView: In situ Visualization of Convolutional Neural Networks

Chen, Xinyu; Guan, Qiang; Lo, Li-Ta; Su, Simon; Ren, Zhengyong; Ahrens, James; Estrada, Trilce

doi:10.1109/bigdata.2018.8622443

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Neural networks are often considered black box methods because of obscure inner workings with features and parameters not explicitly guided by human hands. The models described herein are no different, although new research is showing indications of increasing interpretability of results by using methods such as activation map visualization (Yosinski, Clune, Nguyen, Fuchs, & Lipson, 2015; Zintgraf, Cohen, Adel, & Welling, 2017; Chen et al, 2018) to understand what influences network decisions.…”

Section: Discussionmentioning

confidence: 99%

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Victor¹,

Aghajan²,

Sewart³

et al. 2019

Psychological Assessment

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Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect, and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: AiME (Artificial Intelligence Mental Evaluation). AiME consists of a short human-computer interactive evaluation and artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Due to its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation.

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Section: Discussionmentioning

confidence: 99%

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Victor¹,

Aghajan²,

Sewart³

et al. 2019

Psychological Assessment

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“…Other techniques that focus visualization on the learning process rather than the magnitude of the error are Deep Tracker [9] and Tensor view [10].…”

Section: Data Visualizationmentioning

confidence: 99%

Neural Network Design using a Virtual Reality Platform

Bibbò¹,

Morabito²

2022

GJCST

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The evolution of Deep Learning (DL), a subset of machine learning, has made their use very effective in many artificial intelligence(AI) fields. In parallel Virtual Reality is going wide in many applications thanks to the proliferation of cameras in mobile devices and improved processing efficiency. Data visualization in deep learning is a fundamental element for which it can benefit from the advantages offered by the visualization of the VR for the development of the models. In addition, the researchers can widely use the editing of images and videos in the machine learning process to design a convolutional network suitable for image recognition. In this study, we want to demonstrate the usefulness of this approach in collecting data within virtual reality to train and optimize a convolutional neural network used to recognize human activities (HAR).

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“…There is a growing interest in new developments at the intersection of machine learning and scientific visualization (Bertini and Lalanne, 2009). Some contributions integrate visualization in certain steps of machine learning to customize training of models (Chen et al, 2017b;Li et al, 2018;Liu et al, 2019), while some integrate machine learning algorithms into visualization (Chalupa and Mikulka, 2018;Chauhan et al, 2020;Lasso et al, 2020;Leventhal et al, 2019;Tzeng and Ma, 2005). This work concentrates on the latter case, where the objective is to enhance the visualization process and gain better insight into the given data.…”

Section: Related Workmentioning

confidence: 99%

Toward Data-Driven Filters in Paraview

Maharjan

Zaspel

2022

J Flow Vis Image Proc

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Scientific visualization is a crucial part of many computational workflows in science and engineering. A given visualization is obtained by applying a series of filters or transformations to input data and finally mapping the filtered data to a graphical representation. So far, such filters mostly use deterministic algorithms to process the data. In this work, we aim at extending this methodology toward data-driven filters, filters that expose the abilities of pretrained machine learning models to the visualization system. The use of such data-driven filters can be of particular interest in fields like segmentation, classification, and surrogate models, where machine learning models often outperform existing algorithmic approaches. The objective of this work is to give a starting point for developments toward data-driven filters in multipurpose visualization tools. To this end, we propose, develop, and provide a first software prototype that combines the standard visualization tool ParaView with the state-of-the-art deep learning framework PyTorch in order to expose machine learning models as data-driven filters. Several simple use cases for segmentation and classification filters are presented, showing the technical applicability of our approach. The resulting prototype is provided as open source to facilitate the future development of data-driven filters.

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In situ TensorView: In situ Visualization of Convolutional Neural Networks

Cited by 6 publications

References 18 publications

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Neural Network Design using a Virtual Reality Platform

Toward Data-Driven Filters in Paraview

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