Abstract: The last half-decade has seen a surge in deep learning research on irregular domains ... has been published that improves the inferential power and computational efficiency of graph-based ...

Moniel is one of many attempts at creating a notation for deep learning models leveraging graph thinking. Instead of defining computation as list of formulea, we define the model as a declarative ...

TensorFlow (Abadi et al, 2016) is the most recent deep learning framework developed by Google. The software is written in C++ and offers interfaces to Python. Similar to Theano, a neural network is ...

What is machine learning? Everything you need to know What is deep learning ... and also hyped point/technology: Knowledge graphs. The term "knowledge graph" is essentially a rebranding of ...

attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social ...

As applications of deep learning widen in many industries ... and compiling them from eager execution to optimized computational graph running on CPU or FPGA backends may not be feasible due ...