Plant diseases remain a major threat to food security and agricultural productivity, especially in resource-constrained ...

At its core, deep learning is inspired by a simplified model of how the human brain works by building effective hierarchical representations of complex data. This course will explore applications and ...

Become a Member The center’s faculty seeks active engagement toward building a robust, comprehensive, and scalable solution for an end-to-end deep learning training and model-serving architecture.

Traditional object detection algorithms, including both two-stage models like Faster R-CNN and one-stage variants such as ...

the researchers identified a model that, when coupled with appropriate data augmentation and optimization partner, may help aid in the detection of skin cancer. Using an accessible deep learning ...

Background Coronary artery disease (CAD) is linked to an increased risk of mild cognitive impairment (MCI). Effective and ...

We’re developing a deep learning model that should be able to predict the plasma’s behavior in a split second ... When I say “AI Scientist,” I really mean an AI scientific assistant. The literature ...

A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...