Update README.md

README.md

@@ -1,19 +1,11 @@
 # Feature Selection in Neural Networks
 Demo code for the LassoNet method proposed in "LassoNet: A Neural Network with Feature Sparsity".
 
-For more details, see the accompanying paper: ["LassoNet: A Neural Network with Feature Sparsity"](https://arxiv.org/abs/1907.12207), *AISTATS 2021*, and please use the citation below.
-
-```
-@article{lemhadri2019neural,
-  title={A neural network with feature sparsity},
-  author={Lemhadri, Ismael and Ruan, Feng and Tibshirani, Robert},
-  journal={arXiv preprint arXiv:1907.12207},
-  year={2019}
-}
-```
-<p align="center">
-  <img height="500" src="examples/fig1.png">
-</p>
+## Supporting material
+- [Paper](https://arxiv.org/pdf/1907.12207.pdf)
+- Video tutorial [[short version](https://www.youtube.com/watch?v=G5vPojso9PU)] [[long version](https://www.youtube.com/watch?v=ztGcoMPazwc)]
+- [Slides](https://ismael.lemhadri.org/papers/pdf/lassonet_slides.pdf)
+- [Poster](https://ismael.lemhadri.org/papers/pdf/lassonet_poster.pdf)
 
 ## Intro
 Much work has been done recently to make neural networks more interpretable, and one obvious approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or L1-regularized) regression assigns zero weights to the most irrelevant or redundant features, and is widely used in data science. However the Lasso only applies to linear models. Here we introduce LassoNet, a neural network framework with global feature selection. Our approach enforces a hierarchy: specifically a feature can participate in a hidden unit only if its linear representative is active. Unlike other approaches to feature selection for neural nets, our method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly. As a result, it delivers an entire regularization path of solutions with a range of feature sparsity. On systematic experiments, LassoNet significantly outperforms state-of-the-art methods for feature selection and regression. The LassoNet method uses projected proximal gradient descent, and generalizes directly to deep networks. It can be implemented by adding just a few lines of code to a standard neural network.
@@ -42,9 +34,21 @@ See core/lassonet/lassonet_trainer.py and core/lassonet/models.py for details.
 
 One png file will be saved in your directory, which is a visualization of the results on the MICE dataset. An interactive version is provided in the notebook ```examples/run_demo_mice.ipynb```. 
 
-<!-- 
-See [the paper](https://arxiv.org/abs/1907.12207) for details on the Mice dataset. The paper also presents more experiments on real-world datasets.
--->
+## Citation
+For more details, see the accompanying paper: ["LassoNet: A Neural Network with Feature Sparsity"](https://arxiv.org/abs/1907.12207), *AISTATS 2021*, and please use the citation below.
+
+```
+@article{lemhadri2019neural,
+  title={A neural network with feature sparsity},
+  author={Lemhadri, Ismael and Ruan, Feng and Tibshirani, Robert},
+  journal={arXiv preprint arXiv:1907.12207},
+  year={2019}
+}
+```
+<p align="center">
+  <img height="500" src="examples/fig1.png">
+</p>
+
 
 ## Acknowledgments
 Thanks and acknowledgments to [Louis Abraham](https://louisabraham.github.io/) for the PyTorch version of LassoNet. This repository heavily borrows on [Louis Abraham's repository](https://github.com/louisabraham/DeepLasso).