In this paper, we propose a novel weight initialization method to address this issue. We establish several properties of our initial weight matrix.

Nov 7, 2023 · In this paper, we propose a novel weight initialization method to address this issue. We establish several properties of our initial weight matrix.

Apr 1, 2024 · Generally, in narrow networks with fewer nodes, learning is less effective compared to wider networks with a larger number of nodes. However, ...

Improved weight initialization for deep and narrow feedforward neural network. Hyunwoo Leea, Yunho Kimb, Seung Yeop Yangc, Hayoung Choia,∗

In this paper, we propose a novel weight initialization method to address this issue. We establish several properties of our initial weight matrix and ...

Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in ...

Apr 7, 2024 · Proper initialization helps achieve faster convergence and improves the neural network's overall performance. Importance of Weight ...

Improved weight initialization for deep and narrow feedforward neural network · no code implementations • 7 Nov 2023 • Hyunwoo Lee, Yunho Kim, Seung Yeop Yang ...

Improved weight initialization for deep and narrow feedforward neural network. https://doi.org/10.1016/j.neunet.2024.106362 ·. Journal: Neural Networks, 2024, p ...

Jan 12, 2013 · It's a good idea to choose initial weights of a neural network from the range (−1 √ d , 1 √ d ) , where d is the number of inputs to a given neuron.