A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on...

the task space and facilitate problem solving. Siamese neural network is composed of two twin networks whose output is jointly trained. There is a function...

Recurrent neural networks (RNNs) are a class of artificial neural networks for sequential data processing. Unlike feedforward neural networks, which process...

fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based...

and relational similarities between words. Siamese Networks: Siamese networks are a type of neural network architecture commonly used for similarity-based...

November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate. The neural network consisted of two main blocks, an...

specifying multiple negatives (multiple negatives ranking loss). Siamese neural network t-distributed stochastic neighbor embedding Learning to rank Similarity...

the MNIST database. She is also a co-inventor of the siamese neural networks, a neural network architecture used to learn similarities, with applications...

model of biological neurons in a neural network. Artificial neurons are the elementary units of artificial neural networks. The artificial neuron is a function...

of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural networks, for each function...

or Nvidia's Tensor core. These developments have greatly accelerated neural network architectures and increased the size and complexity of models that can...

result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised...

transformed, denoted by I = T ( I L ) {\displaystyle I=T(I_{L})} . A Siamese neural network works in tandem on two different input vectors to compute comparable...

for training neural networks to compute the network parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation...

vectors of real numbers. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic...

machines Replicator neural networks, autoencoders, variational autoencoders, long short-term memory neural networks Bayesian networks Hidden Markov models...

Patrik O. (2002). Non-negative sparse coding. Proc. IEEE Workshop on Neural Networks for Signal Processing. arXiv:cs/0202009. Leo Taslaman & Björn Nilsson...

Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into...

connectomics and network neuroscience, have found the small-worldness of neural networks to be associated with efficient communication. In neural networks, short...

statistical and neural networks, on the other hand, have many advantages over the symbolic approach: both statistical and neural networks methods can focus...

(Second ed.). SIAM. ISBN 978-0898716597. Schmidhuber, Jürgen (2015). "Deep learning in neural networks: An overview". Neural Networks. 61: 85–117. arXiv:1404...

study of Artificial Neural Networks(ANNs), specifically deep neural networks(DNNs). It describes the tendency of deep neural networks to fit target functions...

demonstrating the first applicability of stochastic gradient descent to neural networks. Backpropagation was first described in 1986, with stochastic gradient...

between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum...

Rahul Rama Varior, Mrinal Haloi, Gang Wang, (2016) Gated Siamese Convolutional Neural Network Architecture for Human Re-identification, European Conference...

leveraging the powerful function approximation capabilities of deep neural networks, deep BSDE addresses the computational challenges faced by traditional...

universal approximation theorem for artificial neural networks with sigmoid activation functions. SIAM Fellow (2020), "for contributions to theory and...

(2020-12-01). "High-dimensional dynamics of generalization error in neural networks". Neural Networks. 132: 428–446. doi:10.1016/j.neunet.2020.08.022. ISSN 0893-6080...

Laura A. (2018-12-04). ""It's so Cute I Could Crush It!": Understanding Neural Mechanisms of Cute Aggression". Frontiers in Behavioral Neuroscience. 12:...

using deep neural networks in nine of its highest-traffic languages, including all of its speech languages and Japanese. Neural networks provide better...