15 citations · 34 across the 14 of their papers we have counts for
16 papers
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…
Curvature-Aware Training for Coordinate Networks
Hemanth Saratchandran, Shin-Fang Chng, Sameera Ramasinghe +2
Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities. However, training these networks with first-ord…
On progressive sharpening, flat minima and generalisation
Lachlan Ewen MacDonald, Jack Valmadre, Simon Lucey
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning. Specifically, we use existing empirical analys…
Flow supervision for Deformable NeRF
Chaoyang Wang, Lachlan Ewen MacDonald, Laszlo A. Jeni +1
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision. We overcome the major challenge with respect to the computationally ine…
On skip connections and normalisation layers in deep optimisation
Lachlan Ewen MacDonald, Jack Valmadre, Hemanth Saratchandran +1
We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architecture choices including ba…
On Quantizing Implicit Neural Representations
Cameron Gordon, Shin-Fang Chng, Lachlan MacDonald +1
The role of quantization within implicit/coordinate neural networks is still not fully understood. We note that using a canonical fixed quantization scheme during training produces…