Reversible programs run backward as easily as they run forward, saving energy in theory. After decades of research, they may ...

Solve graph-structured data and problems A gated propagation model to compute node representations Unroll recurrence for a fixed number of steps and use backpropogation through time An output model to ...

Stay in control by standing upright on the machine, leaning neither forward nor backward, and look straight ahead. Gradually increase the resistance or gradient on the elliptical machine to ...

By embedding the physical topology of industrial processes into a semi-heterogeneous graph perception network (SHGPN) and incorporating gradient-weighted class activation mapping (Grad-CAM), the ...

In this article, we explore the building of multilayer neural networks based on an efficient gradient-free learning scheme offering a potential solution to the architectural design. The proposed ...

The closure should return a loss or an iterator with its first element as the loss. Momentum here is the moving average of gradient so that its setting is decoupled from the learning rate, which is ...