Bayesian deep neural networks in the proportional regime as renormalized kernel gaussian processes
Project description
Compute expected predictor of a Bayesian deep neural networks in the proportional regime in a non-parametric way using the equivalent GP
Project details
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
deepbays-0.1.11.tar.gz
(28.4 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
deepbays-0.1.11-py3-none-any.whl
(33.8 kB
view details)
File details
Details for the file deepbays-0.1.11.tar.gz.
File metadata
- Download URL: deepbays-0.1.11.tar.gz
- Upload date:
- Size: 28.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e9b41ea215afa6dd37386b6690d6d8d20c5181bb373f6dc43f9a88c764367456
|
|
| MD5 |
e376f2ccfee06174503495b3ced5975b
|
|
| BLAKE2b-256 |
fafc153a9be7e8ddc49f48bb96410043ea41fa1d7f1d0ee253217cbd332579d6
|
File details
Details for the file deepbays-0.1.11-py3-none-any.whl.
File metadata
- Download URL: deepbays-0.1.11-py3-none-any.whl
- Upload date:
- Size: 33.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.10.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fc26c4b254ab2f27aa6ee9ca3e9324c860da4432f6bfd1fd22901e2017b51138
|
|
| MD5 |
e760dfa2755e7ac667384d533eff5413
|
|
| BLAKE2b-256 |
0d61a2add711b1c513d0a9f4c869b020c5db9bc9eb774c6812332ddb26c78876
|