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Simple Regularisation for Uncertainty-Aware Knowledge Distillation


May 19, 2022
Martin Ferianc, Miguel Rodrigues

* Accepted to the ICML 2022 Workshop on Distribution-Free Uncertainty Quantification. The code can be found at: https://github.com/martinferianc/hydra_plus 

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On Causal Inference for Data-free Structured Pruning


Dec 19, 2021
Martin Ferianc, Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Quentin Cappart

* Accepted to ITCI'22: The AAAI-22 Workshop on Information-Theoretic Methods for Causal Inference and Discovery 

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Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator


Nov 24, 2021
Hongxiang Fan, Martin Ferianc, Zhiqiang Que, He Li, Shuanglong Liu, Xinyu Niu, Wayne Luk


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High-Performance FPGA-based Accelerator for Bayesian Recurrent Neural Networks


Jun 04, 2021
Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, Miguel Rodrigues

* 9 pages. Martin Ferianc and Zhiqiang Que share an equal contribution 

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High-Performance FPGA-based Accelerator for Bayesian Neural Networks


May 12, 2021
Hongxiang Fan, Martin Ferianc, Miguel Rodrigues, Hongyu Zhou, Xinyu Niu, Wayne Luk

* Design Automation Conference (DAC) 2021 

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ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation


Apr 14, 2021
Martin Ferianc, Divyansh Manocha, Hongxiang Fan, Miguel Rodrigues

* 12 pages 

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On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks


Feb 22, 2021
Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues

* Code at: https://github.com/martinferianc/quantised-bayesian-nets 

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VINNAS: Variational Inference-based Neural Network Architecture Search


Jul 12, 2020
Martin Ferianc, Hongxiang Fan, Miguel Rodrigues

* Submitted to ICPR'20 https://github.com/iiml-ucl/vinnas 

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