Search Results for "train"

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. ...

Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior...

TCNOpen is an open source initiative which the partner railway industries created with the aim to build in collaboration some key parts of new or upcoming railway standards, commonly known under the name TCN. TCN (Train Communication Network) is a series of international standards (IEC61375) developed by Working Group 43 of the IEC (International Electrotechnical Commission), specifying a communication system for the data communication within and between vehicles of a train. It is currently in use on many thousands of trains in the world in order to allow electronic devices to exchange information while operating aboard the same train. ...

Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained...

...This information can be used for: - obtaining the knowledge which applications are most frequently used in the network - providing the users some basic statistics about their Internet connection usage (for example for which kinds of applications their connection is used the most) - creating scientific profiles of traffic generated by different applications or different groups of applications - creating a traffic generator, to imitate traffic generated by particular applications, or to imitate the real traffic in the network - implementing smart assessment of QoS in the network at the users' level and in the core of the network - obtaining precise data needed to train Machine Learning Algorithms - many more cases :-)

A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.

Infect is a network traffic classification tool, that classifies TCP protocols. The classification is based on ML techniques. Scen: Train/Recognize arbitrary protocols (SSH, HTTPS..), Detect policy violations (e.g. tunnels)