For the high-order Taylor expansion of neural networks, we provide two implementations, HOPE and Autograd. Users can conveniently compute high-order derivatives of neural networks and obtain Taylor ...
derivative is a Python package for differentiating noisy data. The package showcases a variety of improvements that can be made over finite differences when data is not clean. Kaptanoglu et al., (2022 ...
Achieving feasible, smooth and efficient trajectories for autonomous vehicles which appropriately take into account the long-term future while planning, has been a long-standing challenge. Several ...
Investigating the postural balance and stability of standing passengers of public transport in laboratory or numerical tests requires generic test pulses, which ...
Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...
Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population’s growth rate is typically ...
A line drawing of the Internet Archive headquarters building façade. An illustration of a magnifying glass. An illustration of a magnifying glass.
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