Source for csinva.io

Slides • Research overviews • Cheat sheets • Notes
Posts • Research links • 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

• ML slides (berkeley cs 189)
• AI slides (berkeley cs 188)
• Interpretability workshop
• Disentanngled interpretations

Research and class notes

The research_ovws folder contains overviews and summaries of recent papers in different research areas

• Interpretability
• Causal inference
• Transfer learning
• Uncertainty
• DL theory
• Complexity
• Scattering transform
• DL in neuroscience

The _notes folder contains markdown notes and cheat-sheets for many different courses and areas between computer science, statistics, and neuroscience

• Interpretability cheat sheet
• Computational neuroscience
• Causal inference notes
• Classification
• Linear algebra
• Info theory
• Computer vision
• Way more notes here

Code

Links/explanations of research code, such as these repos:

Interpretable machine learning	Interpretable deep learning	Deep learning fun
imodels: transparent model fitting, 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

Reference

• For updates, star the repo or follow @csinva_
• Feel free to use openly!
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