1 citations · 3 across the 16 of their papers we have counts for
5 papers · 2 filters
Efficient Learning With Sine-Activated Low-rank Matrices
Yiping Ji, Hemanth Saratchandran, Cameron Gordon +2
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learn…
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations
Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran +1
Deep implicit functions have been found to be an effective tool for efficiently encoding all manner of natural signals. Their attractiveness stems from their ability to compactly r…
A Sampling Theory Perspective on Activations for Implicit Neural Representations
Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko +2
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional enco…
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Recently, neural networks utilizing periodic activation functions have been proven to demonstrate superior performance in vision tasks compared to traditional ReLU-activated networ…
Architectural Strategies for the optimization of Physics-Informed Neural Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep lear…