Contribute to coallaoh/Principles development by creating an account on GitHub.

Contribute to coallaoh/Principles development by creating an account on GitHub.

Contribute to coallaoh/Principles development by creating an account on GitHub.

Optimal transport is a mathematical theory at the interface between optimization and probability theory. It is a natural tool to study probability distributions in the many situations where they appea...

Leveraging artificial intelligence to enhance educational tools, personalize learning experiences, and improve educational outcomes.

Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detecti...

This link will take you to a page that’s not on LinkedIn

It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al., 2022). This means t...

It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al., 2022). This means t...

Explore the most trending research topics in AI

Explore the most trending research topics in AI

Leverage the power of large language models to process diverse tables and perform various tabular tasks based on natural language instructions.

LLM agents use LLMs as the main controller to execute complex tasks, integrating modules like planning, memory, and tool usage.

Models that can understand and generate both visual and textual information.

AI for Law transforms legal practices with tools and resources for legal text analysis, research, and decision-making.

Synthetic data refers to artificially generated data that mimics real-world data. It may be used for training or evaluating models.

Bayesian Deep Learning is a field that combines Bayesian probability theory with deep learning. It aims to provide a principled way of quantifying uncertainty in neural network predictions.

Models that can understand and generate both visual and textual information.

Human-ML Interaction a data-centric perspective Seong Joon Oh

Explore the most trending research topics in AI