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Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures.
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TLDR
In this article, the authors present Rapid Exploration of Model Architectures and Parameters, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures.Abstract:
Deep learning models require the configuration of many layers and parameters in order to get good results. However, there are currently few systematic guidelines for how to configure a successful model. This means model builders often have to experiment with different configurations by manually programming different architectures (which is tedious and time consuming) or rely on purely automated approaches to generate and train the architectures (which is expensive). In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures. In REMAP, the user explores the large and complex parameter space for neural network architectures using a combination of global inspection and local experimentation. Through a visual overview of a set of models, the user identifies interesting clusters of architectures. Based on their findings, the user can run ablation and variation experiments to identify the effects of adding, removing, or replacing layers in a given architecture and generate new models accordingly. They can also handcraft new models using a simple graphical interface. As a result, a model builder can build deep learning models quickly, efficiently, and without manual programming. We inform the design of REMAP through a design study with four deep learning model builders. Through a use case, we demonstrate that REMAP allows users to discover performant neural network architectures efficiently using visual exploration and user-defined semi-automated searches through the model space.read more
Citations
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Journal ArticleDOI
A survey of visual analytics techniques for machine learning
TL;DR: A taxonomy of visual analytics techniques is built, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building.
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
Angelos Chatzimparmpas,Rafael Messias Martins,Ilir Jusufi,Kostiantyn Kucher,Fabrice Rossi,Andreas Kerren +5 more
TL;DR: This survey is intended to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
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Multi-view deep learning for zero-day Android malware detection
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PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
Jorge Piazentin Ono,Sonia Castelo,Roque Lopez,Enrico Bertini,Juliana Freire,Cláudio T. Silva +5 more
TL;DR: The Pipeline Profiler is an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning pipelines produced by AutoML systems, providing users a better understanding of the algorithms that generated them as well as insights into how they can be improved.
Proceedings ArticleDOI
Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bauerle,Ángel Alexander Cabrera,Fred Hohman,Megan Maher,David Koski,Xavier Suau,Titus Barik,Deborah Moritz +7 more
TL;DR: Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards, was designed and implemented.
References
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Proceedings ArticleDOI
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Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.