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martinferianc/README.md

๐Ÿ‘‹ Hi there! I'm Martin

Website โ€ข Google Scholar โ€ข LinkedIn


Martin obtained an MEng in Electronic and Information Engineering from Imperial College London, London, UK in 2015. He is currently a PhD candidate in the Department of Electronic and Electrical Engineering at University College London. His research interests include Bayesian neural networks, deep learning , hardware acceleration and confidence calibration. He has hands-on experience from industrial/academic placements in different countries.

Pinned

  1. yamle yamle Public

    YAMLE: Yet Another Machine Learning Environment

    Python 31

  2. Renate Renate Public

    Forked from awslabs/Renate

    Library for automatic retraining and continual learning

    Python

  3. quantised-bayesian-nets quantised-bayesian-nets Public

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

    Python 18 2

  4. Improving_Per_Esti_for_FPGA-based_Acc_for_CNNs-ARC2020 Improving_Per_Esti_for_FPGA-based_Acc_for_CNNs-ARC2020 Public

    A codebase accompanying the paper "Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks", by Ferianc et al. presented at ARC'2020

    Jupyter Notebook 11 2

  5. ComBiNet ComBiNet Public

    ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

    Python 17 1

  6. hydra_plus hydra_plus Public

    Simple Regularisation for Uncertainty-Aware Knowledge Distillation

    Python 2