csinva/vscode-browser-html-preview
Preview static HTML pages inside VS Code tabs, served by the Live Server extension, without opening a browser
Seeking superhuman explanations. Prev @MicrosoftResearch & AI PhD from UC Berkeley.
Preview static HTML pages inside VS Code tabs, served by the Live Server extension, without opening a browser
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Matching in GAN latent space for better bias benchmarking and semantic image editing. πΆπ»π§πΎπ©πΌβπ¦°π±π½ββοΈπ΄πΎ
Agent-interpretable data-science tools, evolved via autoresearch (NeurIPS 2026).
Slides, paper notes, class notes, blog posts, and research on ML π, statistics π, and AI π€.
tqdm-style progress bars in the Claude Code statusline for any long-running job. Claude picks how to observe progress; the ETA corrects itself; crashes come back with a skull and a report.
Interpret text data with LLMs (sklearn compatible).
Generating paper titles (and more!) with GPT trained on data scraped from arXiv.
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" π (ICLR Workshop 2020)
Interpretations on the HPA dataset.
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" π§ (ICLR 2019)
Tree prompting: easy-to-use scikit-learn interface for improved prompting.
Finding semantically meaningful and accurate prompts.
readme
Let the agent write the weights, why not?
Clean website for simple self-quizzing.
Generating and validating natural-language explanations for the brain.
Automatically message copilot cli to keep going whenever it stops (useful for autoresearch loops).
Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)
Clone of pyfim making it installable as a dependency. Copied from http://www.borgelt.net/pyfim.html
A logical, reasonably standardized, but flexible project structure for conducting ml research πͺ
News Balancer takes a story and provides articles on that story with credibility and varying political bias. The homepage will randomly generate a story from its archives, but a user can type in a query to get stories relating to their query along with their credibility / political bias.
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
Testing the properties of ensembled neural networks.
Exploring ways to extract stable interpretations from neural networks.
Building and vetting clinical decision rules.
Preprocessed data for various popular tabular datasets to go along with imodels.