Search Results for author: Martin Ferianc

Found 8 papers, 2 papers with code

Simple Regularisation for Uncertainty-Aware Knowledge Distillation

no code implementations19 May 2022 Martin Ferianc, Miguel Rodrigues

We demonstrate the generality of the approach on combinations of toy data, SVHN/CIFAR-10, simple to complex NN architectures and different tasks.

BIG-bench Machine Learning Knowledge Distillation

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator

no code implementations24 Nov 2021 Hongxiang Fan, Martin Ferianc, Zhiqiang Que, He Li, Shuanglong Liu, Xinyu Niu, Wayne Luk

Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.

Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator

no code implementations4 Jun 2021 Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, Miguel Rodrigues

To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.

Time Series Analysis

High-Performance FPGA-based Accelerator for Bayesian Neural Networks

no code implementations12 May 2021 Hongxiang Fan, Martin Ferianc, Miguel Rodrigues, HongYu Zhou, Xinyu Niu, Wayne Luk

Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems.

Autonomous Vehicles Bayesian Inference +2

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

1 code implementation14 Apr 2021 Martin Ferianc, Divyansh Manocha, Hongxiang Fan, Miguel Rodrigues

Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation.

Bayesian Inference Decision Making +2

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

1 code implementation22 Feb 2021 Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues

Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation.

Autonomous Driving Decision Making

VINNAS: Variational Inference-based Neural Network Architecture Search

no code implementations12 Jul 2020 Martin Ferianc, Hongxiang Fan, Miguel Rodrigues

In recent years, neural architecture search (NAS) has received intensive scientific and industrial interest due to its capability of finding a neural architecture with high accuracy for various artificial intelligence tasks such as image classification or object detection.

Image Classification Neural Architecture Search +3

Cannot find the paper you are looking for? You can Submit a new open access paper.