Abstract: Selecting an appropriate step size is critical in Gradient Descent algorithms used to train Neural Networks for Deep Learning tasks. A small value of the step size leads to slow convergence, ...
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
In autonomous driving, understanding the 3D world over time is critical. Yet, most vision-based 3D Occupancy (VisionOcc) methods only scratch the surface of temporal fusion, focusing on simple ...
Abstract: The problem of designing adaptive stepsize sequences for the gradient descent method applied to convex and locally smooth functions is studied. We take an adaptive control perspective and ...
State-of-the-art techniques for pavement performance evaluation have attracted considerable attention in recent years. Artificial Neural Networks (ANNs) can simulate the human brain to discover hidden ...
If you need support for a new econometric algorithm or have an idea for an implementation, please submit your request via GitHub Issues. After evaluation, we'll add it to our DEVPLAN for future ...
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