Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...

Graph-based learning techniques traditionally focus on pairwise relationships, modelling them as edges between two nodes. Hypergraphs generalise this concept by allowing edges—known as hyperedges—to ...