We thank Evan Sparks and Josh Rosen for their many contributions to deepViz, a project that served as the basis for development of ML-o-
AbstractThe recent success of deep learning is driving a trend towards structurally complex computer vision models that combine feature extraction with predictive elements into integrated pipelines. While some of these models have achieved breakthrough results in applications like object recognition, they are difficult to design and tune, impeding progress. We feel that visual analysis can be a powerful tool to aid iterative development of deep model pipelines. Building on feature evaluation work in the computer vision community, we introduce ML-o-scope, an interactive visualization system for exploratory analysis of convolutional neural networks, a prominent type of pipelined model. We present ML-o-scope's time-lapse engine that provides views into model dynamics during training, and evaluate the system as a support for tuning large scale object-classification pipelines.
Abstract-Large-scale text data research has recently started to regain momentum [1]- [10], because of the wealth of up to date information communicated in unstructured format. For example, new information in online media (e.g. Web blogs, Twitter, Facebook, news feeds, etc) becomes instantly available and is refreshed regularly, has very broad coverage and other valuable properties unusual for other data sources and formats. Therefore, many enterprises and individuals are interested in integrating and using unstructured text in addition to their structured data.DATA TAMER, introduced in [11] is a new data integration system for structured data sources. Its features include a schema integration facility, an entity consolidation module and a unique expert-sourcing mechanism for obtaining human guidance. Also, included are a capability for data cleaning and transformations.Here we describe a new scalable architecture and extensions enabling DATA TAMER to integrate text with structured data.
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