2 research outputs found
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego
Deep Neural Networks (DNN) have been widely employed in industry to address
various Natural Language Processing (NLP) tasks. However, many engineers find
it a big overhead when they have to choose from multiple frameworks, compare
different types of models, and understand various optimization mechanisms. An
NLP toolkit for DNN models with both generality and flexibility can greatly
improve the productivity of engineers by saving their learning cost and guiding
them to find optimal solutions to their tasks. In this paper, we introduce
NeuronBlocks\footnote{Code: \url{https://github.com/Microsoft/NeuronBlocks}}
\footnote{Demo: \url{https://youtu.be/x6cOpVSZcdo}}, a toolkit encapsulating a
suite of neural network modules as building blocks to construct various DNN
models with complex architecture. This toolkit empowers engineers to build,
train, and test various NLP models through simple configuration of JSON files.
The experiments on several NLP datasets such as GLUE, WikiQA and CoNLL-2003
demonstrate the effectiveness of NeuronBlocks.Comment: 6 pages, 3 figure
NeuralVis: Visualizing and Interpreting Deep Learning Models
Deep Neural Network(DNN) techniques have been prevalent in software
engineering. They are employed to faciliatate various software engineering
tasks and embedded into many software applications. However, analyzing and
understanding their behaviors is a difficult task for software engineers. In
this paper, to support software engineers in visualizing and interpreting deep
learning models, we present NeuralVis, an instance-based visualization tool for
DNN. NeuralVis is designed for: 1). visualizing the structure of DNN models,
i.e., components, layers, as well as connections; 2). visualizing the data
transformation process; 3). integrating existing adversarial attack algorithms
for test input generation; 4). comparing intermediate outputs of different
instances to guide the test input generation; To demonstrate the effectiveness
of NeuralVis, we conduct an user study involving ten participants on two
classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can
assist developers in identifying the critical features that determines the
prediction results. Video: https://youtu.be/hRxCovrOZF