Abstract: Deep Recurrent Neural Network (DRNN) is an effective deep learning method with a wide variety of applications. Manually designing the architecture of a DRNN for any specific task requires ...

This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel. We focus on ...

Lynne Eldrige, MD, is a lung cancer physician, patient advocate, and award-winning author of "Avoiding Cancer One Day at a Time." Doru Paul, MD, is triple board-certified in medical oncology, ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

Image recognition has made significant progress in recent years, majorly in the development of powerful algorithms that can analyze and interpret visual data with unparalleled accuracy. In this ...

Neural networks are powerful tools for processing visual inputs, but precisely how this processing is performed remains unclear. We introduce a recurrent neural network that can perform simple image ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

Abstract: As revealed by the no free lunch theorem, no single algorithm can outperform any others on all classes of optimization problems. To tackle this issue, methods for recommending an existing ...

Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs studied are the Elman, gated recurrent unit (GRU), and long short-term ...