Martin Ferianc

According to our database, Martin Ferianc authored at least 26 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Large language models surpass human experts in predicting neuroscience results.

Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K. Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N. Apurva Ratan Murty, Leyla Loued-Khenissi, Anna Behler, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love

CoRR, 2024

SAE: Single Architecture Ensemble Neural Networks.

Martin Ferianc, Hongxiang Fan, Miguel Rodrigues

CoRR, 2024

YAMLE: Yet Another Machine Learning Environment.

Martin Ferianc, Miguel Rodrigues

CoRR, 2024

2023

Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions.

Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip C. Treleaven, Miguel Rodrigues

CoRR, 2023

Impact of Noise on Calibration and Generalisation of Neural Networks.

Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues

CoRR, 2023

Renate: A Library for Real-World Continual Learning.

Martin Wistuba, Martin Ferianc, Lukas Balles, Cédric Archambeau, Giovanni Zappella

CoRR, 2023

An Online Learning Method for Microgrid Energy Management Control^*.

Vittorio Casagrande, Martin Ferianc, Miguel Rodrigues, Francesca Boem

Proceedings of the 31st Mediterranean Conference on Control and Automatio, 2023

MIMMO: Multi-Input Massive Multi-Output Neural Network.

Martin Ferianc, Miguel Rodrigues

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations.

Hongxiang Fan, Martin Ferianc, Zhiqiang Que, Xinyu Niu, Miguel R. D. Rodrigues, Wayne Luk

IEEE Trans. Parallel Distributed Syst., 2022

Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs.

Shuanglong Liu, Hongxiang Fan, Martin Ferianc, Xinyu Niu, Huifeng Shi, Wayne Luk

IEEE Trans. Neural Networks Learn. Syst., 2022

FPGA-Based Acceleration for Bayesian Convolutional Neural Networks.

Hongxiang Fan, Martin Ferianc, Zhiqiang Que, Shuanglong Liu, Xinyu Niu, Miguel R. D. Rodrigues, Wayne Luk

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

Simple Regularisation for Uncertainty-Aware Knowledge Distillation.

Martin Ferianc, Miguel Rodrigues

CoRR, 2022

Enabling fast uncertainty estimation: accelerating bayesian transformers via algorithmic and hardware optimizations.

Hongxiang Fan, Martin Ferianc, Wayne Luk

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator.

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

Proceedings of the 27th Asia and South Pacific Design Automation Conference, 2022

2021

On Causal Inference for Data-free Structured Pruning.

Martin Ferianc, Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Quentin Cappart

CoRR, 2021

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

Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, Miguel Rodrigues

CoRR, 2021

On the effects of quantisation on model uncertainty in Bayesian neural networks.

Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation.

Martin Ferianc, Divyansh Manocha, Hongxiang Fan, Miguel Rodrigues

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator.

Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, Miguel Rodrigues

Proceedings of the International Conference on Field-Programmable Technology, 2021

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

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

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

2020

VINNAS: Variational Inference-based Neural Network Architecture Search.

Martin Ferianc, Hongxiang Fan, Miguel Rodrigues

CoRR, 2020

Optimizing FPGA-Based CNN Accelerator Using Differentiable Neural Architecture Search.

Hongxiang Fan, Martin Ferianc, Shuanglong Liu, Zhiqiang Que, Xinyu Niu, Wayne Luk

Proceedings of the 38th IEEE International Conference on Computer Design, 2020

Improving Performance Estimation for FPGA-Based Accelerators for Convolutional Neural Networks.

Martin Ferianc, Hongxiang Fan, Ringo S. W. Chu, Jakub Stano, Wayne Luk

Proceedings of the Applied Reconfigurable Computing. Architectures, Tools, and Applications, 2020

2019

Static Block Floating-Point Quantization for Convolutional Neural Networks on FPGA.

Hongxiang Fan, Gang Wang, Martin Ferianc, Xinyu Niu, Wayne Luk

Proceedings of the International Conference on Field-Programmable Technology, 2019

F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition.

Hongxiang Fan, Cheng Luo, Chenglong Zeng, Martin Ferianc, Zhiqiang Que, Shuanglong Liu, Xinyu Niu, Wayne Luk

Proceedings of the 30th IEEE International Conference on Application-specific Systems, 2019

2018

A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA.

Hongxiang Fan, Shuanglong Liu, Martin Ferianc, Ho-Cheung Ng, Zhiqiang Que, Shen Liu, Xinyu Niu, Wayne Luk

Proceedings of the International Conference on Field-Programmable Technology, 2018