134 citations · 160 across the 16 of their papers we have counts for
11 papers · 1 filter
Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA
Hao Mark Chen, Liam Castelli, Martin Ferianc +4
Reliable uncertainty estimation plays a crucial role in various safety-critical applications such as medical diagnosis and autonomous driving. In recent years, Bayesian neural netw…
YAMLE: Yet Another Machine Learning Environment
Martin Ferianc, Miguel Rodrigues
YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods. Th…
SAE: Single Architecture Ensemble Neural Networks
Martin Ferianc, Hongxiang Fan, Miguel Rodrigues
Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. To improve the hardware efficiency of ensembles of…
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks
Martin Ferianc, Ondrej Bohdal, Timothy Hospedales +1
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique…
Renate: A Library for Real-World Continual Learning
Martin Wistuba, Martin Ferianc, Lukas Balles +2
Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indicati…
Simple Regularisation for Uncertainty-Aware Knowledge Distillation
Martin Ferianc, Miguel Rodrigues
Considering uncertainty estimation of modern neural networks (NNs) is one of the most important steps towards deploying machine learning systems to meaningful real-world applicatio…
On Causal Inference for Data-free Structured Pruning
Martin Ferianc, Anush Sankaran, Olivier Mastropietro +2
Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required numb…
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Hongxiang Fan, Martin Ferianc, Zhiqiang Que +4
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware desig…
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
Martin Ferianc, Zhiqiang Que, Hongxiang Fan +2
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have man…
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc, Partha Maji, Matthew Mattina +1
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quanti…
VINNAS: Variational Inference-based Neural Network Architecture Search
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 accurac…