Learnable box filters and precomputed summed-area tables enable efficient arbitrarily large kernel convolutions in fully-convolutional networks while maintaining constant parameters per filter and competitive performance on human pose estimation.
Adam: A method for stochastic optimization
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NeuroPlastic is a gradient-based optimizer augmented with a multi-signal plasticity modulation mechanism that improves performance over standard updates on image classification tasks, especially in low-data regimes.
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Accelerating Large-Kernel Convolution Using Summed-Area Tables
Learnable box filters and precomputed summed-area tables enable efficient arbitrarily large kernel convolutions in fully-convolutional networks while maintaining constant parameters per filter and competitive performance on human pose estimation.
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NeuroPlastic: A Plasticity-Modulated Optimizer for Biologically Inspired Learning Dynamics
NeuroPlastic is a gradient-based optimizer augmented with a multi-signal plasticity modulation mechanism that improves performance over standard updates on image classification tasks, especially in low-data regimes.