Source for csinva.io
Slides β’
Research overviews β’
Cheat sheets β’
Notes
Posts β’
Research links β’
Personal info
@csinva_
Slides
The pres folder contains source for presentations, including ML slides from teaching machine learning at berkeley
The source is in markdown (built with reveal-md) and is easily editable / exportable
Research and class notes
The research_ovws folder contains overviews and summaries of recent papers in different research areas
The _notes folder contains markdown notes and cheat-sheets for many different courses and areas between computer science, statistics, and neuroscience
Code
Links/explanations of research code, such as these repos:
| Interpretable machine learning | Interpretable deep learning | Deep learning fun |
|---|---|---|
| imodels: transparent model library (e.g. FIGS + HS), DAC: disentangled attribution curves | ACD: hierarchical interpretations, TRIM: interpreting transformations, CDEP: penalizing explanations, AWD: adaptive wavelet distillation | GAN/VAE: demo models, paper-title generator with gpt2 |
Posts
Posts on various aspects of machine learning / statistics / neuroscience advancements
| Interpretability | Connectomics | Disentanglement |
|---|---|---|
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Reference
- For updates, star the repo or follow @csinva_
- Feel free to use openly!
- Built with jekyll | github pages | timeline theme | particles.js | jupyterbook