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Found 6 papers, 1 papers with code
Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective
Vision transformer (ViT) is an attention neural network architecture that is shown to be effective for computer vision tasks.
TAME: Task Agnostic Continual Learning using Multiple Experts
Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner.
ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals
The core of our approach is the novel detect-then-segment method for raw radar signals.
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded
CC-FCN has two types of input layers, one is for the particle images, and the other is for the initial velocity field calculated using cross-correlation with a coarse resolution.
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a growing interest in addressing the inherent suboptimality of heuristic approaches such as CART.
