Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from ...

A research team led by the University of Aberdeen has developed a pioneering AI model to improve accuracy and reduce ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to ...

Open-source clones of ChatGPT can be fine-tuned at scale and with limited or no expertise, facilitating ‘private' language ...

By looking at an AI model's internal representations — the numbers that dictate how an AI model responds, which often seem ...

used imaging data from the Human Connectome Project to align neural activity with its underlying circuitry. Mapping how the brain's anatomical connections and activity patterns relate to behavior ...

The neural network is designed as an autoencoder, consisting of three types of neural blocks: convolutional, downsampling, and upsampling blocks. All the blocks have leaky ReLU and batch normalization ...

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

The proposed method uses Autoencoder based Deep Neural Network (AEDNN) trained with NSL-KDD dataset to efficiently detect possible cyber threats. This paper proposed AEDNN to detect automated threats ...

To fit subtle cross-spatiotemporal interactions and learn pathological features from fMRI, we proposed a fine-grained spatiotemporal graph neural network with self ... were proposed for the ...