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Interactive Streamlit workbench for visualizing eyetracking-while-reading scanpaths, computing reading easures, and exporting figures and tabular data.
Python suite for neuroscience research across all modalities.
AI reviewing paper drafts for improvement.
AI-powered citation search & paper review for Overleaf — Chrome extension. Think Google Scholar but inside Overleaf. Also works with OpenAI Prism, & Opera.
Hundreds of models & providers. One command to find what runs on your hardware.
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Python package for Zuna, an EEG foundation model for inference.
Easy Sweeps: A command-line utility for automating W&B sweep creation, launch, and GPU process management with ease.
A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP
PaperBanana: Automating Academic Illustration For AI Scientists
EyeBench: Predictive Modeling from Eye Movements in Reading
Algorithms for the automated correction of vertical drift in eye-tracking data
This repository contains the Potsdam Textbook Corpus (PoTeC) which is a natural reading eye-tracking corpus.
OneStop: A 360-Participant Eye Tracking Dataset with Different Reading Regimes
📊 A simple command-line utility for querying and monitoring GPU status
⏰ AI conference deadline countdowns
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
A python package for processing eye movement data
Machine learning metrics for distributed, scalable PyTorch applications.
Typed argument parser for Python
🎓 Academic portfolio that boosts citations. AI generates pages, you own as Markdown. BibTeX auto-import, Jupyter, LaTeX, slides, visual block editor — free to host forever. 学术主页，AI 生成，Markdown 拥有 👇
🧱 Describe your site, AI builds it, you own it as Markdown. Snap together Tailwind blocks like Lego — landing pages, blogs, portfolios, docs & more. No AI slop. Free to deploy anywhere 👇
A playbook for systematically maximizing the performance of deep learning models.
Multimodal model for text and tabular data with HuggingFace transformers as building block for text data