167 citations · 780 across the 46 of their papers we have counts for
5 papers · 2 filters
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…
Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs
Sameera Ramasinghe, Simon Lucey
Coordinate-MLPs are emerging as an effective tool for modeling multidimensional continuous signals, overcoming many drawbacks associated with discrete grid-based approximations. Ho…
Rethinking Positional Encoding
Jianqiao Zheng, Sameera Ramasinghe, Simon Lucey
It is well noted that coordinate based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier feat…
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization
Damien Teney, Ehsan Abbasnejad, Simon Lucey +1
Neural networks trained with SGD were recently shown to rely preferentially on linearly-predictive features and can ignore complex, equally-predictive ones. This simplicity bias ca…
Reframing Neural Networks: Deep Structure in Overcomplete Representations
Calvin Murdock, George Cazenavette, Simon Lucey
In comparison to classical shallow representation learning techniques, deep neural networks have achieved superior performance in nearly every application benchmark. But despite th…