Abstract: Training deep/convolutional neural networks (DNNs/CNNs) requires a large amount of memory and iterative computation, which necessitates speedup and energy reduction, especially for edge ...
Abstract: Various studies have been undertaken to learn point cloud representations that are both discriminative and robust. However, most of them suffer from rotation disturbance and insufficient ...
Abstract: In recent years, deep neural networks (DNNs) have brought revolutionary progress in various fields with the advent of technology. It is widely used in image pre-processing, image enhancement ...
Abstract: Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successfully applied to train deep neural networks. Despite its advantages in terms of reducing ...
The company highlights early puppy training, owner education, and free consultations to help new owners build structure ...
Blue Vault, a leading aggregator of alternative investment performance data and analysis, today announced the launch of its new research portal, delivering a streamlined, data-rich way for investment ...
Abstract: In this work we address the problem of 3D object detection from point clouds in data-limited environments. Training with simulated data is a common approach in such scenarios; however a ...
Abstract: This work provides a tutorial to model a nonlinear system by means of a set of linear parameter varying (LPV) systems, linearized around equilibrium points in which the control design will ...
Microsoft's GitHub next month plans to begin using customer interaction data – "specifically inputs, outputs, code snippets, and associated context" – to train its AI models. The code locker’s revised ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results