2 citations · 5 across the 9 of their papers we have counts for
4 papers · 1 filter
Sparsifying networks by traversing Geodesics
Guruprasad Raghavan, Matt Thomson
The geometry of weight spaces and functional manifolds of neural networks play an important role towards 'understanding' the intricacies of ML. In this paper, we attempt to solve c…
Architecture Agnostic Neural Networks
Sabera Talukder, Guruprasad Raghavan, Yisong Yue
In this paper, we explore an alternate method for synthesizing neural network architectures, inspired by the brain's stochastic synaptic pruning. During a person's lifetime, numero…
Self-organization of multi-layer spiking neural networks
Guruprasad Raghavan, Cong Lin, Matt Thomson
Living neural networks in our brains autonomously self-organize into large, complex architectures during early development to result in an organized and functional organic computat…
Geometric algorithms for predicting resilience and recovering damage in neural networks
Guruprasad Raghavan, Jiayi Li, Matt Thomson
Biological neural networks have evolved to maintain performance despite significant circuit damage. To survive damage, biological network architectures have both intrinsic resilien…