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Update README.md
charlesmartin14 May 25, 2025
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addd conda installation recipe
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46 changes: 40 additions & 6 deletions README.md
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@@ -1,5 +1,6 @@
[![Downloads](http://pepy.tech/badge/weightwatcher)](http://pepy.tech/project/weightwatcher)
[![PyPI](https://img.shields.io/pypi/v/weightwatcher?color=teal&label=release)](https://pypi.org/project/weightwatcher/)
[![Conda](https://img.shields.io/conda/v/conda-forge/weightwatcher?color=green)](https://anaconda.org/conda-forge/weightwatcher)
[![GitHub](https://img.shields.io/github/license/calculatedcontent/weightwatcher?color=blue)](./LICENSE.txt)
[![Published in Nature](https://img.shields.io/badge/Published%20in-Nature-teal)](https://nature.com/articles/s41467-021-24025-8)
[![Video Tutorial](https://img.shields.io/badge/Video-Tutorial-blue)](https://www.youtube.com/watch?v=Tnafo6JVoJs)
Expand Down Expand Up @@ -45,6 +46,10 @@ And in the notebooks provided in the [examples](https://github.com/CalculatedCon
```sh
pip install weightwatcher
```
Or, via conda:
```sh
conda install conda-forge::weightwatcher
```

if this fails try

Expand Down Expand Up @@ -439,7 +444,7 @@ details = watcher.distances(initial_model, trained_model)

---

#### compatability with version 0.2.x
#### Compatibility with version 0.2.x

The new 0.4.x version of WeightWatcher treats each layer as a single, unified set of eigenvalues.
In contrast, the 0.2.x versions split the Conv2D layers into n slices, one for each receptive field.
Expand Down Expand Up @@ -543,9 +548,14 @@ This tool is based on state-of-the-art research done in collaboration with UC Be
<summary>
WeightWatcher has been featured in top journals like JMLR and Nature:
</summary>
#### Latest papers and talks

### Latest papers and talks

- [Grokking and Generalization Collapse: Insights from HTSR theory (available upon request)]

- [SETOL: A Semi-Empirical Theory of (Deep) Learning (draft)] (https://github.com/CalculatedContent/setol_paper/blob/main/setol_draft.pdf)

- [SETOL: A Semi-Empirical Theory of (Deep) Learning] (in progress)
- [Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training (NeurIPS 2023 Spotlight Paper)(https://arxiv.org/abs/2312.00359)

- [Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics](https://arxiv.org/abs/2106.00734)

Expand Down Expand Up @@ -591,7 +601,11 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
- [KDD 2019 Workshop: Statistical Mechanics Methods for Discovering
Knowledge from Production-Scale Neural Networks](https://dl.acm.org/doi/abs/10.1145/3292500.3332294)

- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf)
- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf)

#### NeurIPS 2023
- [Heavy-Tailed Self-Regularization in Deep Neural Networks](https://neurips.cc/virtual/2023/83033)


</details>

Expand All @@ -600,7 +614,7 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
WeightWatcher has also been featured at local meetups and many popular podcasts
</summary>

#### Popular Popdcasts and Blogs
#### Popular Podcasts and Blogs

- [This Week in ML](https://twimlai.com/meetups/implicit-self-regularization-in-deep-neural-networks/)

Expand All @@ -618,16 +632,36 @@ WeightWatcher has also been featured at local meetups and many popular podcasts

- [Applied AI Community](https://www.youtube.com/watch?v=xLZOf2IDLkc&feature=youtu.be)

- [UCL Financial Computing (2022)](https://www.youtube.com/watch?v=sOXROWJ70Pg)

- [Practical AI](https://changelog.com/practicalai/194)

- [Latest Results](https://www.youtube.com/watch?v=rojbXvK9mJg)
- [AI Nation 2023](https://www.youtube.com/watch?v=rojbXvK9mJg)

- [ICCF 2024](https://youtu.be/_c0-_ru0sZc)

- [Data Science at Home (2025)](https://www.youtube.com/watch?v=iv7Pv3StHms)

- [Cohere for AI 2025](https://www.youtube.com/watch?v=NXqO4nDNIwo)

- [The FreeStyle Podcast](https://www.youtube.com/watch?v=hb0YrwQ3K2Q)

- [This Week in ML AI Podcast](https://twimlai.com/podcast/twimlai/grokking-generalization-collapse-and-the-dynamics-of-training-deep-neural-networks/)


and many more



#### 2021 Short Presentations

- [MLC Research Jam March 2021](presentations/ww_5min_talk.pdf)

- [PyTorch2021 Poster April 2021](presentations/pytorch2021_poster.pdf)

#### TEDx Talk
- [The Emergence of Signatures of Artificial General Intelligence ](https://www.youtube.com/watch?v=5dBEzqTlq-Y)

#### Recent talk(s) by Mike Mahoney, UC Berekely

- [IARAI, the Institute for Advanced Research in Artificial Intelligence](https://www.youtube.com/watch?v=Pirni67ZmRQ)
Expand Down