We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to

In DM, kernels play an important role for capturing the nonlinear relationship of data.

In this work, we propose a novel 3D Transformer framework called LART for 3D motion transfer.

Neural networks are state-of-the-art classification approaches but are generally difficult to interpret. This issue can be partly alleviated by constructing a precise decision process within the neural network. In this work, a network architecture, denoted as Classification-By-Components network (CBC), is proposed.

The line, triangular and square shapes represent the joint distribution of two, three and four variables respectively.