Graph neural networks (GNNs) can be pictured as a special class of neural network models where data are structured as graphs — both training data used to train the model and real-world data used for inference — rather than fixed-size vectors or grids like image, sequences, or instances of tabular data.
Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient …
Read more “Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment”
Large language models (LLMs) are changing the way we think about AI.
This post is divided into three parts; they are: • Setting up the translation pipeline • Translation with alternatives • Quality estimation Text translation is a fundamental task in natural language processing, and it inspired the invention of the original transformer model.
Natural language processing models including the wide variety of contemporary large language models (LLMs) have become popular and useful in recent years as their application to a wide variety of problem domains have become increasingly capable, especially those related to text generation.
Be sure to check out the previous articles in this series: •
For many people studying data science,
Debugging machine learning models entails inspecting, discovering, and fixing possible errors in the internal mechanisms of these models.
The transformers library is a Python library that provides a unified interface for working with different transformer models.
Large language models (LLMs) are a big step forward in artificial intelligence.