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Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
Suman Sapkota
Artificial Neural Networks of varying architectures are generally paired with affine transformation at the core. However, we find dot product neurons with global influence less int…
Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation
Suman Sapkota, Binod Bhattarai
The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these…
Importance Estimation with Random Gradient for Neural Network Pruning
Suman Sapkota, Binod Bhattarai
Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the…
Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
Suman Sapkota, Binod Bhattarai
Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is ge…
Input Invex Neural Network
Suman Sapkota, Binod Bhattarai
Connected decision boundaries are useful in several tasks like image segmentation, clustering, alpha-shape or defining a region in nD-space. However, the machine learning literatur…