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Update README.md to include correct citation #46

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ZüNIS: Normalizing flows for neural importance sampling
==============================

ZüNIS (Zürich Neural Importance Sampling) a work-in-progress Pytorch-based library for Monte-Carlo integration
based on Neural imporance sampling [[1]](https://arxiv.org/abs/1808.03856), developed at ETH Zürich.
ZüNIS (Zürich Neural Importance Sampling)[[1]](https://arxiv.org/abs/2401.09069) a work-in-progress Pytorch-based library for Monte-Carlo integration
based on Neural imporance sampling [[2]](https://arxiv.org/abs/1808.03856), developed at ETH Zürich.
In simple terms, we use artificial intelligence to compute integrals faster.

The goal is to provide a flexible library to integrate black-box functions with a level of automation comparable to the
VEGAS Library [[2]](https://pypi.org/project/vegas/), while using state-of-the-art methods that go around
VEGAS Library [[3]](https://pypi.org/project/vegas/), while using state-of-the-art methods that go around
the limitations of existing tools.

If you are using ZüNIS, please cite arXiv:2401.09069.

## Installation

### Using `pip`
Expand Down Expand Up @@ -66,4 +68,4 @@ The function `f` is integrated over the `d`-dimensional unit hypercube and
* takes `torch.Tensor` batched inputs with shape `(N,d)` for arbitrary batch size `N` on `device`
* returns `torch.Tensor` batched inputs with shape `(N,)` for arbitrary batch size `N` on `device`

A more systematic documentation is under construction [here](https://zunis.readthedocs.io).
A more systematic documentation is under construction [here](https://zunis.readthedocs.io).