Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

1 code implementation • 7 Sep 2022 • Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso

Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.

Anatomy Anomaly Detection

Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip

no code implementations • ICLR 2018 • Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie

Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network.

NMT speech-recognition +1

Optimizing Performance of Recurrent Neural Networks on GPUs

1 code implementation • 7 Apr 2016 • Jeremy Appleyard, Tomas Kocisky, Phil Blunsom

As recurrent neural networks become larger and deeper, training times for single networks are rising into weeks or even months.

ACDC: A Structured Efficient Linear Layer

2 code implementations • 18 Nov 2015 • Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas

Finally, this paper also provides a connection between structured linear transforms used in deep learning and the field of Fourier optics, illustrating how ACDC could in principle be implemented with lenses and diffractive elements.