This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...
The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most ...
Abstract: Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable ...
Abstract: To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does not ...
What does it take to get OpenAI and Anthropic—two competitors in the AI assistant market—to get along? Despite a fundamental difference in direction that led Anthropic’s founders to quit OpenAI in ...
For the past decade, AI researcher Chris Olah has been obsessed with artificial neural networks. One question in particular engaged him, and has been the center of his work, first at Google Brain, ...
The following is adapted from Walter Isaacson's biography "Elon Musk," publishing Sept. 12. On a Friday in late August of this year, Elon Musk got into his Model S at Tesla headquarters in Palo Alto, ...
The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...
Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...
We investigate the similarities between two of the most challenging and complex systems in Nature: the network of neuronal cells in the human brain, and the cosmic network of galaxies. We explore the ...
Since AlexNet was invented in 2012, there has been rapid development in convolutional neural network architectures in computer vision. Representative architectures (Figure 1) include GoogleNet (2014), ...
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