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readme.md

source for csinva.io

slidesresearch overviewscheat sheetsnotes
postsresearch linkspersonal info
@csinva_

slides

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 overviews

overviews and summaries of recent papers in different research areas in the research_ovws folder (e.g. ml interpretability, theory, complexity, scattering transform, dl in neuroscience)

notes

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

interp

code

links to 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 GAN/VAE: demo models, paper-title generator with gpt2

acd_intro

posts

posts on various aspects of machine learning / statistics / neuroscience advancements

interpretability connectomics disentanglement

reference

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