Name
Abstract | ||
|---|---|---|
We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the grap... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TVCG.2017.2744878 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
Visualization,Layout,Machine learning,Computational modeling,Tools,Neural networks,Standards | Graph drawing,Graph database,Source code,Computer science,Theoretical computer science,Dataflow,Artificial intelligence,Deep learning,Artificial neural network,Graph (abstract data type),Debugging | Journal |
Volume | Issue | ISSN |
24 | 1 | 1077-2626 |
Citations | PageRank | References |
43 | 1.21 | 33 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanit Wongsuphasawat | 1 | 317 | 10.95 |
| Daniel Smilkov | 2 | 48 | 1.66 |
| James Wexler | 3 | 80 | 4.40 |
| Jimbo Wilson | 4 | 53 | 1.64 |
| Dandelion Mane | 5 | 43 | 1.21 |
| Doug Fritz | 6 | 43 | 1.21 |
| Dilip Krishnan | 7 | 761 | 30.16 |
| Fernanda B. Viégas | 8 | 3208 | 283.62 |
| Martin Wattenberg | 9 | 4695 | 333.69 |