@beaglebasher yes a few of us have met him at/after the agm... has anyone met you would be the right question now... if you have seen the amount of research put up by many here including FF, thats a very sorry allegation to be made.. anyways again I hope it was an honest mistake.. if not... the other website is the perfect place for you...Are you serious?
You got your answer. Now what?I have met nobody in person from this forum. That should be obvious..
I asked a sensitive question and I would appreciate an honest answer.
But wait there is more. The names on this im pretty sure are Korean... Pub date July 2022
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Ok so this is pretty Sweet
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Take your allegations else whereI was making no allegations. I was asking an honest question.
I hope you got your answer..I was making no allegations. I was asking an honest question.
I always knew we where GreenMemristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems
Researchers create memristors with honey.semiengineering.com
Honey might become a crucial feature to build brain-like computer chips - Interesting Engineering
You read that right.interestingengineering.com
Well the honest answer is yes. I don't know how to articulate that any other way tbhI have met nobody in person from this forum. That should be obvious..
I asked a sensitive question and I would appreciate an honest answer.
Yes I have. As much integrity in person as evident over the keyboard.I have met nobody in person from this forum. That should be obvious..
I asked a sensitive question and I would appreciate an honest answer.
You should think or at least read before you post.I have met nobody in person from this forum. That should be obvious..
I asked a sensitive question and I would appreciate an honest answer.
Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism. To further improve the performance of MLP, we introduce information communication mechanisms from brain-inspired neural networks. Spiking Neural Network (SNN) is the most famous brain-inspired neural network, and achieve great success on dealing with sparse data. Leaky Integrate and Fire (LIF) neurons in SNNs are used to communicate between different time steps. In this paper, we incorporate the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and 83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively, which are state-of-the-art results as far as we know.
Comments: | This paper is accepted by CVPR 2022 |
Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
Cite as: | arXiv:2203.14679 [cs.CV] |
(or arXiv:2203.14679v1 [cs.CV] for this version) | |
https://doi.org/10.48550/arXiv.2203.14679 Focus to learn more |
Developing neuromorphic intelligence on event-based datasets with spiking neural networks (SNNs) has recently attracted much research attention. However, the limited size of event-based datasets makes SNNs prone to overfitting and unstable convergence. This issue remains unexplored by previous academic works. In an effort to minimize this generalization gap, we propose neuromorphic data augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance. The proposed method is simple and compatible with existing SNN training pipelines. Using the proposed augmentation, for the first time, we demonstrate the feasibility of unsupervised contrastive learning for SNNs. We conduct comprehensive experiments on prevailing neuromorphic vision benchmarks and show that NDA yields substantial improvements over previous state-of-the-art results. For example, NDA-based SNN achieves accuracy gain on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively.
Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
Cite as: | arXiv:2203.06145 [cs.CV] |
(or arXiv:2203.06145v1 [cs.CV] for this version) | |
https://doi.org/10.48550/arXiv.2203.06145 Focus to learn more |
Great find, think I need a bigger screen though....
Technology Award 2022: Mercedes Vision EQXX | Auto Express
The Mercedes Vision EQXX is the 2022 Auto Express Technology Award winnerwww.autoexpress.co.uk
Clowns don't last long around here... so don't worry.Stop replying to him and spamming the pages which is what he wants
Answer is you invest because you invest. Fact finder is real and if you have noticed he has been around quite a LONG time. There is a Company called Brainchip which you may have heard about, i think that's the name which should interest you the most not random forum names who spend hours of research to manipulate you Lol o boy ive been manipulated into manipulation. lol sorry but i can't stop laughing.I have met nobody in person from this forum. That should be obvious..
I asked a sensitive question and I would appreciate an honest answer.