Haut PDF Deep learning in event-based neuromorphic systems
Deep learning in event-based neuromorphic systems
... bio-inspired learning algorithms (different variants of STDP), which are applied on populations of inhibitory and excitatory leaky integrate-and-fire ...neurons. Learning in their implemen- tation is ...
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Handling the speed-accuracy trade-off in deep-learning based pedestrian detection systems
... Work In this thesis we focus on simultaneously improving the detection accuracy and detection speed of deep learning based pedestrian detection ...of deep learning based ...
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A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision
... be in the field of artificial ...processing systems face severe limitations imposed both by the conventional sensors front-ends (which produce very large amounts of data with fixed sampled frame-rates), and ...
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Deep learning for sentiment and event-driven REIT price dynamics
... progress in this field is to find out better representative features of textual ...features based on the Chi-Square ...structured event tuples, and they develop an unsupervised method to learn ...
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Towards Debugging and Testing Deep Learning Systems
... performance in terms of prediction abil- ity regarding manually labeled data and–or automatically generated ...inconsistencies in the behavior of the model under test; so whenever inconsis- tencies are ...
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Distribution-Based Invariant Deep Networks for Learning Meta-Features
... presented in this section considers two goals of experiments: (i) assessing the ability of Dida to learn accurate meta-features; (ii) assessing the merit of the Dida invariant layer design, building invariant f ϕ ...
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New perspectives on plant disease characterization based on deep learning
... significantly in recent years due to globaliza- tion, trade, climate change and the reduction in the resilience of pro- duction systems due to decades of agricultural ...losses in crops, ...
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Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator
... advancements in computational neu- roscience, spiking neuromorphic hardware has gained momentum over the last years ...timing-based learning rules like Spike-Timing- Dependent Plasticity ...
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Efficient FPGA-Based Inference Architectures for Deep Learning Networks
... Published in: Journal of Signal Processing Systems (JSPS) 2019 [13] Abstract–Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) have gained significant popularity in several ...
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Deep Learning Based Traffic Signs Boundary Estimation
... detection in order to detect traffic signs boundaries for a better ...boundaries. In this kind of approach, the coordinates of the boundaries corners can be detected across multiple consecutive frames, and ...
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ANALYSIS OF COMMON DESIGN CHOICES IN DEEP LEARNING SYSTEMS FOR DOWNBEAT TRACKING
... used in training to avoid ...is based on a multi-band spectral flux, computed using the short time Fourier transform with a Hann window, using a hop-size of 10ms and a window length of 2048 samples, with a ...
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en fr Neuromorphic analysis of hemodynamics using event-based cameras Analyse neuromorphique de l’hémodynamique avec des caméras évènementielles
... how neuromorphic sensors can give new in- sights into medical imaging in particular in the study of hemodynam- ...study in depth, in a non-invasive way, the dynamics of ...an ...
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Impact of PCM resistance-drift in neuromorphic systems and drift-mitigation strategy
... PCM BASED SYNAPTIC A RCHITECTURES ...[6]. In this approach, we use two PCM devices to implement a single synapse and connect them in a com- plementary configuration to the post-synaptic output ...
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Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform
... detection systems. Barlow and Levick ( 1965 ) described a pulse-based mechanism similar to the one proposed by Kramer ( 1996 ) where direction selectivity derives from lateral asymmetric inhibition ...
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Solving dynamical systems in neuromorphic hardware: simulation studies using balanced spiking networks
... dynamical systems using neuromorphic ...[1]. In this framework, recurrent spiking net- work of Leaky Integrate-and-Fire (LIF) neurons can track solution of a linear dynamical system by minimizing ...
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An Event-Based Approach to Runtime Adaptation in Communication-Centric Systems
... including event selectors (a building block in event-driven systems) and trans- formations between multithreaded and event-driven ...work in [10] introduces session set types to ...
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Overview of deep-learning based methods for salient object detection in videos
... some deep-learning based SOD mod- els derive their good performance or gains from well-established knowledge of 505 traditional ...modules in recent image SOD networks [43, 44] and the video ...
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Entity-centric representations in deep learning
... advances in deep reinforcement learning are in part driven by a capacity to learn good representations that can be used by an agent to update its ...of learning representations of the ...
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Deep-learning based segmentation of challenging myelin sheaths
... Terms—deep learning, segmentation, myelin, axon, g- ratio, convolutional neural network (CNN), electron microscopy ...NTRODUCTION In the central nervous system, white matter consists of myelinated ...
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Variance Based Samples Weighting for Supervised Deep Learning
... Variance Based Sample Weighting (VBSW) that weights each training data points using the local variance of their neighbor labels to simulate the new ...explored in many works and for various ...problem. ...
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