readme,docs: add poster

README.md

@@ -2,20 +2,22 @@
 
 This package offers drop-in implementations of PyTorch `nn.Module`s.
 They are as fast as their built-in equivalents, but more memory-efficient whenever you want to compute gradients for a sub-set of parameters (i.e. some have `requires_grad=False`).
-You can convert your neural network by calling the `memsave_torch.nn.convert_to_memory_saving` function.
+You can convert your neural network by calling the [`memsave_torch.nn.convert_to_memory_saving`](https://memsave-torch.readthedocs.io/en/stable/api/nn/index.html#memsave_torch.nn.convert_to_memory_saving) function.
 
 Take a look at the [Basic Example](#basic-example) to see how it works.
 
 Currently it supports the following layers:
-- `memsave_torch.nn.MemSaveLinear`
-- `memsave_torch.nn.MemSaveConv2d`
 - `memsave_torch.nn.MemSaveConv1d`
+- `memsave_torch.nn.MemSaveConv2d`
+- `memsave_torch.nn.MemSaveConv3d`
+- `memsave_torch.nn.MemSaveConvTranspose1d`
+- `memsave_torch.nn.MemSaveConvTranspose2d`
+- `memsave_torch.nn.MemSaveConvTranspose3d`
 - `memsave_torch.nn.MemSaveReLU`
 - `memsave_torch.nn.MemSaveBatchNorm2d`
-- `memsave_torch.nn.MemSaveLayerNorm`
 - `memsave_torch.nn.MemSaveMaxPool2d`
 
-Also, each layer has a `.from_nn_<layername>(layer)` function which allows to convert a single `torch.nn` layer into its memory-saving equivalent. (e.g. `MemSaveConv2d.from_nn_Conv2d`)
+Also, each layer has a `.from_nn_<layername>(layer)` function which allows to convert a single `torch.nn` layer into its memory-saving equivalent. (e.g. [`MemSaveConv2d.from_nn_Conv2d`](https://memsave-torch.readthedocs.io/en/stable/api/nn/memsave_torch.nn.MemSaveConv2d.html))
 
 ## Installation
 
@@ -45,19 +47,30 @@ loss = loss_func(model(X), y)
 ```
 
 ## Further reading
-- [Link to documentation]()
-- [Link to more examples]()
-- [Link to paper/experiments folder]()
-- [Writeup](memsave_torch/writeup.md)
+
+- [Writeup](https://github.com/plutonium-239/memsave_torch/blob/llm/experiments/writeup.md)
+
+  This explains the basic ideas around MemSave without diving into too many details.
+
+- [Our paper (WANT@ICML'24)](https://openreview.net/pdf?id=KsUUzxUK7N) and it's [Poster](https://github.com/plutonium-239/memsave_torch/blob/main/memsave_poster.pdf)
+
+  It is also available on [arXiv](https://arxiv.org/abs/2404.12406)
+
+- [Documentation](https://memsave-torch.readthedocs.io/)
+<!-- - [Link to more examples]()
+- [Link to paper/experiments folder]()-->
 
 ## How to cite
 
 If this package has benefited you at some point, consider citing
 
 ```bibtex
-
-@article{
-  TODO
+@inproceedings{
+  bhatia2024lowering,
+  title={Lowering PyTorch's Memory Consumption for Selective Differentiation},
+  author={Samarth Bhatia and Felix Dangel},
+  booktitle={2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024)},
+  year={2024},
+  url={https://openreview.net/forum?id=KsUUzxUK7N}
 }
-
 ```

docs_src/index.rst

@@ -103,7 +103,7 @@ Further reading
 
    This explains the basic ideas around MemSave without diving into too many details.
 
-* `Our paper (WANT@ICML'24) <https://openreview.net/pdf?id=KsUUzxUK7N>`_
+* `Our paper (WANT@ICML'24) <https://openreview.net/pdf?id=KsUUzxUK7N>`_ and it's `Poster <https://github.com/plutonium-239/memsave_torch/blob/main/memsave_poster.pdf>`_
 
    It is also available on `arXiv <https://arxiv.org/abs/2404.12406>`_.