Search Results for author: André van Schaik

Found 12 papers, 1 papers with code

Superevents: Towards Native Semantic Segmentation for Event-based Cameras

no code implementations13 May 2021 Weng Fei Low, Ankit Sonthalia, Zhi Gao, André van Schaik, Bharath Ramesh

Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks.

Depth Estimation Semantic Segmentation +1

Event-based Feature Extraction Using Adaptive Selection Thresholds

no code implementations18 Jul 2019 Saeed Afshar, Ying Xu, Jonathan Tapson, André van Schaik, Gregory Cohen

A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations.

Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

no code implementations23 May 2018 Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.

A Stochastic Approach to STDP

no code implementations13 Mar 2016 Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, André van Schaik

The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.

A neuromorphic hardware framework based on population coding

no code implementations2 Mar 2015 Chetan Singh Thakur, Tara Julia Hamilton, Runchun Wang, Jonathan Tapson, André van Schaik

These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.

FPGA Implementation of the CAR Model of the Cochlea

no code implementations2 Mar 2015 Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Richard F. Lyon, André van Schaik

Here, we implement the Cascade of Asymmetric Resonators (CAR) model of the cochlea on an FPGA.

Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm

no code implementations29 Dec 2014 Mark D. McDonnell, Migel D. Tissera, Tony Vladusich, André van Schaik, Jonathan Tapson

Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

General Classification Speech Recognition

Learning ELM network weights using linear discriminant analysis

no code implementations12 Jun 2014 Philip de Chazal, Jonathan Tapson, André van Schaik

We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks.

General Classification

Explicit Computation of Input Weights in Extreme Learning Machines

no code implementations11 Jun 2014 Jonathan Tapson, Philip de Chazal, André van Schaik

In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer.

Online and Adaptive Pseudoinverse Solutions for ELM Weights

no code implementations30 May 2014 André van Schaik, Jonathan Tapson

The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use.

General Classification

ELM Solutions for Event-Based Systems

no code implementations30 May 2014 Jonathan Tapson, André van Schaik

The modifications involve the re-definition of hidden layer units as synaptic kernels, in which the input delta functions are transformed into continuous-valued signals using a variety of impulse-response functions.

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