https://github.com/borealisai/latent-bottlenecked-anp
https://github.com/borealisai/latent-bottlenecked-anp
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/borealisai/latent-bottlenecked-anp
- Owner: BorealisAI
- License: other
- Created: 2023-02-10T17:27:40.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-15T06:13:42.000Z (about 2 years ago)
- Last Synced: 2025-06-02T05:55:08.456Z (about 1 year ago)
- Language: Python
- Size: 574 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Latent Bottlenecked Attentive Neural Processes (LBANPs)
This is the official implementation of the paper [Latent Bottlenecked Attentive Neural Processes](https://arxiv.org/abs/2211.08458).
## Why LBANPs
LBANPs are a new computationally efficient sub-quadratic NP variant, that has a querying computational complexity independent of the number of context datapoints. LBANPs achieve results competitive with the state-of-the-art on meta-regression, image completion, and contextual multi-armed bandits. Furthermore, they can trade-off the computational cost and performance according to the number of latent vectors while also scaling beyond existing attention-based NP variants to larger dataset settings.
## Install
Create and activate a conda environment. Install the dependencies as listed in `requirements.txt`:
```
conda create --name lbanp python=3.7
conda activate lbanp
pip install -r requirements.txt
```
## Usage
Please check the directory of each task for specific usage.
## Reference
For technical details, please check the conference version of our paper.
```
@inproceedings{
feng2023latent,
title={Latent Bottlenecked Attentive Neural Processes},
author={Leo Feng and Hossein Hajimirsadeghi and Yoshua Bengio and Mohamed Osama Ahmed},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=yIxtevizEA}
}
```
## Acknowledgement
This code is based on the official code base of [Transformer Neural Processes](https://github.com/tung-nd/TNP-pytorch) (which is based on the official code base of [Bootstrapping Neural Processes](https://github.com/juho-lee/bnp)). We also use related parts from the Pytorch implementation of [Perceiver](https://github.com/lucidrains/Perceiver-pytorch).