PyTorch implementation of reverse-accurate ODE solvers for Neural ODEs
Project description
PyTorch implementation of two papers: (1) Adaptive checkpoint adjoint method for gradient estimation in Neural ODEs (2) MALI: a memory efficient and reverse accurate integrator for Neural ODEs
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
TorchDiffEqPack-1.0.1.tar.gz
(26.1 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file TorchDiffEqPack-1.0.1.tar.gz.
File metadata
- Download URL: TorchDiffEqPack-1.0.1.tar.gz
- Upload date:
- Size: 26.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
832d9659069414ef450b2699d8c03faeeed6afb2ac4c0cd3065921689549de6c
|
|
| MD5 |
b147206062c2acd2867a6e19814edf95
|
|
| BLAKE2b-256 |
fa38edad0b41cef00bc6bddfeaaf83dada3b0790d52adc7c50fdc2d07af06aa7
|
File details
Details for the file TorchDiffEqPack-1.0.1-py3-none-any.whl.
File metadata
- Download URL: TorchDiffEqPack-1.0.1-py3-none-any.whl
- Upload date:
- Size: 34.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6ce5f9c7aa5e627bc8130172c9b6135460cb03532bf3b1742276541ec4c05311
|
|
| MD5 |
faa2d6ece1e0d896fdf79bfdc36ee228
|
|
| BLAKE2b-256 |
fabb31721405fabfffdf5ff2a3f713bcea78d245e081160490f4780fc7959c3b
|
