36 citations · 97 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
On the effectiveness of neural priors in modeling dynamical systems
Sameera Ramasinghe, Hemanth Saratchandran, Violetta Shevchenko +1
Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their…
How You Start Matters for Generalization
Sameera Ramasinghe, Lachlan MacDonald, Moshiur Farazi +2
Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem. In this paper, we promote a shift of focus towards initializa…
Learning Positional Embeddings for Coordinate-MLPs
Sameera Ramasinghe, Simon Lucey
We propose a novel method to enhance the performance of coordinate-MLPs by learning instance-specific positional embeddings. End-to-end optimization of positional embedding paramet…