​A quantum version of the building block behind neural networks could be exponentially more powerful. By Emerging Technology from the arXiv: https://www.technologyreview.com/…/machine-learning-meet-q…/

An Artificial Neuron Implemented on an Actual Quantum Processor, https://arxiv.org/abs/1811.02266

#artificialinteligence #quantumcomputing #neuralnetworks #machinelearning #processors

🔗 An Artificial Neuron Implemented on an Actual Quantum Processor
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence protocols. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt's `perceptron', but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a perceptron, which shows exponential advantage in encoding resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives remarkably good answers against the expected results. We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns, as a first step towards practical training of artificial quantum neural networks to be efficiently implemented o

#machinelearning #deeplearning #computervision
Explained - Neural Style Transfer Research Paper

🎥 Explained - Neural Style Transfer Research Paper
👁 1 раз ⏳ 776 сек.
#machinelearning #deeplearning #computervision #neuralnetworks #ai
Neural Style Transfer refers to a class of software algorithms that manipulate digital images, or videos, to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks in order to perform the image transformation.

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​Why Real Neurons Learn Faster

A closer look into differences between natural nervous systems & artificial #NeuralNetworks

https://www.codeproject.com/Articles/1275031/Why-Real-Neurons-Learn-Faster

🔗 Why Real Neurons Learn Faster
A closer look into differences between natural nervous systems and artificial neural networks

​DeepGCNs: Making GCNs Go as Deep as CNNs

https://deepai.org/publication/deepgcns-making-gcns-go-as-deep-as-cnns?fbclid=IwAR2edqmWo5uKSGybcgRWW43ov-03resk_as2EoJ52nzeaF_3jSnnV3bxH1o

#DeepAI #neuralnetworks #CNNs ##GCNs

🔗 DeepGCNs: Making GCNs Go as Deep as CNNs
10/15/19 - Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classi...

​Restoring ancient text using deep learning: a case study on Greek epigraphy"
https://arxiv.org/abs/1910.06262
Code: https://github.com/sommerschield/ancient-text-restoration/
#ArtificialIntelligence #DeepLearning #NeuralNetworks

🔗 sommerschield/ancient-text-restoration
Restoring ancient text using deep learning: a case study on Greek epigraphy. - sommerschield/ancient-text-restoration

​Neural Module Networks for Reasoning over Text

Gupta et al.: https://arxiv.org/abs/1912.04971

Code: https://nitishgupta.github.io/nmn-drop/

#NeuralNetworks #Reasoning #SymbolicAI

🔗 Neural Module Networks for Reasoning over Text
Neural Module Network for Reasoning over Text, ICLR 2020

​Efficient Graph Generation with Graph Recurrent Attention Networks
Liao et al.: https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN/
#Graph #NeuralNetworks #NeurIPS #NeurIPS201

🔗 lrjconan/GRAN
Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019 - lrjconan/GRAN

​Deep learning with graph-structured representations
T.N. Kipf : https://dare.uva.nl/search?identifier=1b63b965-24c4-4bcd-aabb-b849056fa76d
#DeepLearning #Graph #NeuralNetworks

🔗 Digital Academic Repository - University of Amsterdam

📃 Feature Learning in Infinite-Width Neural Networks

Feature Learning in Infinite-Width Neural Networks
Greg Yang, Edward J. Hu: https://arxiv.org/abs/2011.14522
#MachineLearning #DisorderedSystems #NeuralNetworks

 Машинное обучение, AI, нейронные сети, Big Data (VK)

Мы в компании ComBox Technology разрабатываем и поддерживаем системы искусственного интеллекта прикладного уровня, и одну из задач, которую мы решили - это защита моделей нейронных сетей от копирования и тиражирования на "краевых" устройствах или в ЦОД. Если вы посмотрите органическую выдачу Google для получения информации о защите моделей нейронных сетей, вы найдете методы с использованием водяных знаков в обучающей выборке или варианты шифрования моделей с расшифровкой до начала инференса. В обоих случаях можно получить исходную модель и использовать ее в других решениях без вашего согласия. Наше видение и решение было сделано совместно с компанией Seculab (на базе аппаратных ключей Senselock EL5). Это помогает создать один автономный переносимый зашифрованный двоичный файл (мы используем инструментарий фреймворка Intel OpenVINO с заменой динамических библиотек на статические) с зашифрованными моделями внутри. В этом случае некоторые редко используемые функции реализуются непосредственно на аппаратном ключе защиты и никто не сможет заменить какие-либо динамические библиотеки, а также может получить модель непосредственно из памяти. Возможно, будет интересно описание проблемы и решения - https://medium.com/@ComBoxTech/neural-networks-protec..

#security #openvino #inference #intel #seculab #senselock #virbox #neuralnetworks #ai