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License: MIT License

1,531 to 1,540 of 1,643 Results

test_layers.py Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Python Source Code - 65.7 KB - MD5: 021263d2ab6fb069fe0a790d9fbb27ad
test_neighborlist.py Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Python Source Code - 8.5 KB - MD5: 975ab95029f95204bd207dade76e9b97
tio2_md.py Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Python Source Code - 1.7 KB - MD5: 5a5e7b28c1789b70414a4cff2a7821ce
tio2.rst Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Unknown - 13.4 KB - MD5: 4c0a987ee23a9936cead9b9983ea03f2
trainer.py Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Python Source Code - 7.7 KB - MD5: e6a640a4d9ac9519921e84f82da54520
train.py.default Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Unknown - 192 B - MD5: 74a3e011f307bfbfe9f80f61504c0cd4
utils.py Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Python Source Code - 5.4 KB - MD5: e031f8dc92eb579663a3121c9919b3ba
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
1README.md Oct 5, 2021 - Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Markdown Text - 6.5 KB - MD5: 68f130a767c01781ac014ef8b22e15e0
200414_cylinder-300-2-basic-paper.py Oct 5, 2021 - Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Python Source Code - 5.7 KB - MD5: 01ec21f3e3f07f33187b224dca902de7

test_layers.py

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Python Source Code - 65.7 KB -

test_neighborlist.py

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Python Source Code - 8.5 KB -

tio2_md.py

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Python Source Code - 1.7 KB -

tio2.rst

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Unknown - 13.4 KB -

trainer.py

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Python Source Code - 7.7 KB -

train.py.default

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Unknown - 192 B -

utils.py

Oct 15, 2021 - Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Python Source Code - 5.4 KB -

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

1README.md

Oct 5, 2021 - Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Markdown Text - 6.5 KB -

200414_cylinder-300-2-basic-paper.py

Oct 5, 2021 - Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Python Source Code - 5.7 KB -

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