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Found 5 papers, 1 papers with code
D'OH: Decoder-Only random Hypernetworks for Implicit Neural Representations
We instead present a strategy for the initialization of run-time deep implicit functions for single-instance signals through a Decoder-Only randomly projected Hypernetwork (D'OH).
On progressive sharpening, flat minima and generalisation
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning.
Flow supervision for Deformable NeRF
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision.
On skip connections and normalisation layers in deep optimisation
We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architecture choices including batch normalisation, weight normalisation and skip connections.
Enabling equivariance for arbitrary Lie groups
Our framework enables the implementation of group convolutions over any finite-dimensional Lie group.
