Name
Title | ||
|---|---|---|
OpenSeq2Seq: extensible toolkit for distributed and mixed precision training of sequence-to-sequence models. |
Abstract | ||
|---|---|---|
We present OpenSeq2Seq - an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/w18-2507 | NLP OPEN SOURCE SOFTWARE (NLP-OSS) |
Field | DocType | Volume |
Modalities,Mixed precision,Computer science,Machine translation,Artificial intelligence,Extensibility,Machine learning | Journal | abs/1805.10387 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oleksii Kuchaiev | 1 | 224 | 10.94 |
| Boris Ginsburg | 2 | 1 | 0.71 |
| Igor Gitman | 3 | 1 | 0.37 |
| Vitaly Lavrukhin | 4 | 4 | 1.78 |
| Carl Case | 5 | 437 | 16.75 |
| Paulius Micikevicius | 6 | 9 | 1.59 |