CoQA (Conversational Question Answering)
CoQA is a dataset designed to facilitate the development of conversational question answering systems. It allows computers to engage in human-like dialogue by answering questions based on provided context.
machine learning
Open X-Embodiment (robotics dataset)
Open X-Embodiment is a comprehensive robotics dataset designed for advancing research in embodied AI and robotics applications.
CIFAR-100
CIFAR-100 is a dataset widely used in machine learning and computer vision, consisting of 60,000 images across 100 classes.
Anthropic (company)
Anthropic is an American artificial intelligence research company focused on developing AI systems with an emphasis on safety and interpretability. Founded in 2021 by former OpenAI employees, the company aims to create reliable and steerable AI technologies.
Locally linear embedding (LLE)
Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique used for data visualization and analysis in machine learning.
Bayesian network
A Bayesian network is a graphical model that represents probabilistic relationships among variables using directed acyclic graphs.
Uniform manifold approximation and projection (UMAP)
Uniform manifold approximation and projection (UMAP) is a dimension reduction technique used in data science for visualizing and interpreting high-dimensional data. It is known for preserving both local and global data structure, making it valuable for exploratory data analysis and machine learning.
CIFAR-10
CIFAR-10 is a widely used dataset in machine learning for image classification, consisting of 60,000 32×32 color images in 10 different classes.
Mila (institute)
Mila is a research institute specializing in artificial intelligence based in Montreal, Canada. It focuses on machine learning and deep learning research and collaborates with academic and industry partners worldwide.
GPT-2
GPT-2 is an advanced language processing AI developed by OpenAI, known for its ability to generate human-like text based on given prompts.