This is your work, valued

Cambridge, MA

Curtis G. Northcutt

Elite
@cgnorthcutt

Director, AI Research @Handshake-AI-Research | PhD @MIT | Creator of cleanlab package | Former Google, Oculus, Amazon, Facebook, Microsoft, NASA AI

benchmarking-keras-pytorch. ๐Ÿ”ฅ Reproducibly benchmarking Keras and PyTorch models

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hypopt. โธ Parallelized hyper-param optimization with validation set, not crossval

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rankpruning. ๐Ÿงน Formerly for binary classification with noisy labels. Replaced by cleanlab.

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cleanlab. Official cleanlab repo is at https://github.com/cleanlab/cleanlab

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confidentlearning-reproduce. Official data release to reproduce Confident Learning paper results

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cnn-gpu-benchmarks. Latest (2020) CNN and GPU Benchmarks on ImageNet and CIFAR

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ieee-keywords. IEEE Computer Society Keywords to Organize Knowledge

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veneer. ๐Ÿ˜Ž Obscures commit history on GitHub.

8

reliablity_framework_for_rag. Demo showing how the Trustworthy Language Model add reliability to LLM outputs and improves RAG, agents, and data enrichment worfklows. can be used to improve fine-tuning of LLMs, accuracy of LLM outputs, and smart routing for RAG and agents.

5

forum-diversification. Add diversity to the order of comments in forums!

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systemspecs. ๐Ÿ’ป Prints GPU, CPU, RAM, storage, etc. for linux/unix systems.

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cgnorthcutt.github.io. Curtis G. Northcutt's personal website.

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ScrapeGitHubEmails. Python

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Awesome-Learning-with-Label-Noise. A curated list of resources for Learning with Noisy Labels

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coteaching_plus. ICML'19: How does Disagreement Help Generalization against Label Corruption?

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SemanticTextDB. OpenEdge ABL

1

documentation-theme-jekyll. A Jekyll-based theme designed for documentation and help systems. See the link for detailed instructions on setting up and configuring everything.

1

streamlit-demo. Uber app demo using streamlit

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snorkel. A system for quickly generating training data with weak supervision

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ribbit.

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l2e-docker. Shell

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creative-theme-jekyll. HTML

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pomdp. The official website of PomDP the PhD rapper.

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iteround. Sum-safe Rounding for Iterables

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pytorch-cnn-finetune. Fine-tune pretrained Convolutional Neural Networks with PyTorch

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scripts. misc useful things

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python-cpu-stress-test-benchmark. Benchmark your CPU multi-thread and single-thread speed without needing sudo.

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imagenet-multiGPU.torch. an imagenet example in torch.

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m2r. Markdown to reStructuredText converter

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cuml. cuML - RAPIDS Machine Learning Library

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dotfiles. Uses dotbot to handle my dotfiles.

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SCELoss-Reproduce. Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112

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tox_cov_travis_pytest_framework. Python

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mixup-cifar10. mixup: Beyond Empirical Risk Minimization

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x. playground for testing stuff on github

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gitignore. A collection of useful .gitignore templates

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project-pages. Fork this repo for a quick start. If "Project Timeline" or "License" appeared on your nav bar, Look Below!

1

scholar.py. A parser for Google Scholar, written in Python

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Replica-Dataset. The Replica Dataset v1 as published in https://arxiv.org/abs/1906.05797 .

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hydejack. "Best Jekyll Theme by a Mile"

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templates. Document templates for open-source projects (README, CONTRIBUTING, GitHub templates)

1

Co-teaching. NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

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PyTorch_CIFAR10. Pretrained TorchVision models on CIFAR10 dataset (with weights)

1

EgoCom-Dataset. EgoCom: A Multi-person Multi-modal Egocentric Communications Dataset

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models. Models and examples built with TensorFlow

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sentence-similarity. This repository contains various ways to calculate sentence vector similarity using NLP models

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imagenet-simple-labels. Simpler human-readable labels for ImageNet ๐Ÿท

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quickdraw-dataset. Documentation on how to access and use the Quick, Draw! Dataset.

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noisy_label_understanding_utilizing. ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

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cameo. A Python Pandas library for cheating, collaboration, proficiency, and retention detection in edX courses ingested with Google BigQuery.

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