2 citations · 3 across the 10 of their papers we have counts for
10 papers
An Empirical Investigation Into the Time and Frequency Response Characteristics of Hopf Resonators
Keziah Milligan, Nicholas J. Bailey, Bernd Porr
We present an empirical investigation of software developed by the Science and Music Research Group at the University of Glasgow. Initially created for musicological applications,…
Prime and Modulate Learning: Generation of forward models with signed back-propagation and environmental cues
Sama Daryanavard, Bernd Porr
Deep neural networks employing error back-propagation for learning can suffer from exploding and vanishing gradient problems. Numerous solutions have been proposed such as normalis…
Touch if it's transparent! ACTOR: Active Tactile-based Category-Level Transparent Object Reconstruction
Prajval Kumar Murali, Bernd Porr, Mohsen Kaboli
Accurate shape reconstruction of transparent objects is a challenging task due to their non-Lambertian surfaces and yet necessary for robots for accurate pose perception and safe m…
AI based analysis of red blood cells in oscillating microchannels
Andreas Link, a Irene Luna Pardo, Bernd Porr +1
The flow dynamics of red blood cells in vivo in blood capillaries and in vitro in microfluidic channels is complex. Cells can obtain differnet shapes such as discoid, parachute, sl…
BCI-Walls: A robust methodology to predict success or failure in brain computer interfaces
Bernd Porr, Lucía Muñoz Bohollo
Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with…
Simulation and Model Checking for Close to Realtime Overtaking Planning
Daumantas Pagojus, Alice Miller, Bernd Porr +1
Fast and reliable trajectory planning is a key requirement of autonomous vehicles. In this paper we introduce a novel technique for planning the route of an autonomous vehicle on a…
Sign and Relevance Learning
Sama Daryanavard, Bernd Porr
Standard models of biologically realistic or biologically inspired reinforcement learning employ a global error signal, which implies the use of shallow networks. On the other hand…
Real-time noise cancellation with Deep Learning
Bernd Porr, Sama Daryanavard, Lucía Muñoz Bohollo +2
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learni…
Reinforcement Learning
Olivier Buffet, Olivier Pietquin, Paul Weng
Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In…
Closed-loop deep learning: generating forward models with back-propagation
Sama Daryanavard, Bernd Porr
A reflex is a simple closed loop control approach which tries to minimise an error but fails to do so because it will always react too late. An adaptive algorithm can use this erro…