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Update arXiv version + citation for OGB-LSC

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PiperOrigin-RevId: 394217992
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Petar Veličković
2021-09-01 14:42:15 +01:00
committed by Saran Tunyasuvunakool
parent 769bfdbeaf
commit 08a26772ec
3 changed files with 10 additions and 7 deletions
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ogb_lsc/README.md
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@@ -5,7 +5,7 @@ This repository contains DeepMind's entry to the [PCQM4M-LSC](https://ogb.stanfo
tracks of the [OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/)
(OGB-LSC).
For full details regarding this entry, please see our [technical report](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## Code structure
@@ -25,5 +25,6 @@ To cite this work:
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
ogb_lsc/mag/README.md
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@@ -3,7 +3,7 @@
This repository contains DeepMind's entry to the [MAG240M-LSC](https://ogb.stanford.edu/kddcup2021/mag240m/) (academic graph) track of the
[OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/) (OGB-LSC).
For full details regarding this entry, please see our [technical report](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## DeepMind MAG Team ("Academic")
@@ -23,7 +23,7 @@ For full details regarding this entry, please see our [technical report](https:/
## Performance
Our final test set performance was achieved by pooling an ensemble of 10 folds.
See [technical report](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf) for details.
See [technical report](https://arxiv.org/abs/2107.09422) for details.
Each model was trained for < 72 hours using 4x Google Cloud TPUv4 and 1x AMD
EPYC 7B12 64-core CPU @2.25GHz.
@@ -140,7 +140,8 @@ To cite this work (together with our PCQM4M-LSC entry):
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
Our technical report can be found [here](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf).
Our technical report can be found [here](https://arxiv.org/abs/2107.09422).
ogb_lsc/pcq/README.md
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@@ -4,7 +4,7 @@ This repository contains DeepMind's entry to the [PCQM4M-LSC](https://ogb.stanfo
track of the [OGB Large-Scale Challenge](https://ogb.stanford.edu/kddcup2021/)
(OGB-LSC).
For full details regarding this entry, please see our [technical report](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf).
For full details regarding this entry, please see our [technical report](https://arxiv.org/abs/2107.09422).
## DeepMind PCQ Team ("Quantum")
@@ -25,7 +25,7 @@ For full details regarding this entry, please see our [technical report](https:/
## Performance
Our final test set performance was achieved by pooling an ensemble of 20 models
(10 folds x 2 seeds). See [technical report](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf) for details.
(10 folds x 2 seeds). See [technical report](https://arxiv.org/abs/2107.09422) for details.
Each model was trained for < 48 hours using 4x Google Cloud TPUv4 and 1x AMD
EPYC 7B12 64-core CPU @2.25GHz.
@@ -110,7 +110,8 @@ To cite this work (together with our MAG240M-LSC entry):
title = {Large-scale graph representation learning with very deep GNNs and
self-supervision},
year = {2021},
journal={arXiv preprint arXiv:2107.09422},
}
```
Our technical report can be found [here](https://storage.googleapis.com/deepmind-ogb-lsc/reports/OGB_LSC_Tech_Report.pdf).
Our technical report can be found [here](https://arxiv.org/abs/2107.09422).