Top 9 transformer network in 2022

Author: builtin.com

Date Submitted: 02/26/2020 12:43 PM

Average star voting: 4 ⭐ ( 29766 reviews)

Summary: The transformer neural network was first proposed in a 2017 paper to solve some of the issues of a simple RNN. This guide will introduce you to its operations.

Match with the search results: . It was first proposed in the paper “Attention Is All You Need.” and is now a state-of-the-art technique in the field of NLP….. read more

Author: en.wikipedia.org

Date Submitted: 06/18/2019 09:02 AM

Average star voting: 4 ⭐ ( 28384 reviews)

Summary: A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.

Match with the search results: A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data ……. read more

Author: blogs.nvidia.com

Date Submitted: 02/09/2020 02:06 AM

Average star voting: 3 ⭐ ( 26507 reviews)

Summary: The transformer is a component used in many neural network designs that takes an input in the form of a sequence of vectors, and converts it into a vector called an encoding, and then decodes it back into another sequence.

Match with the search results: In “Attention Is All You Need”, we introduce the Transformer, a novel neural network architecture based on a self-attention mechanism that ……. read more

Author: www.youtube.com

Date Submitted: 07/26/2021 09:48 PM

Average star voting: 5 ⭐ ( 94974 reviews)

Summary:

Match with the search results: …. read more

Author: deepai.org

Date Submitted: 09/18/2019 07:57 AM

Average star voting: 4 ⭐ ( 77030 reviews)

Summary: A simple algorithm that revolutionizes how neural networks approach language is now taking on image classification as well. It may not stop there.

Match with the search results: A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in ……. read more

Author: machinelearningmastery.com

Date Submitted: 09/30/2019 12:15 AM

Average star voting: 4 ⭐ ( 99125 reviews)

Summary: If you liked this post and want to learn how machine learning algorithms work, how did they arise, and where are they going, I recommend the following: Transformers are a type of neural network…

Match with the search results: www.youtube.com › watch…. read more

Author: www.quantamagazine.org

Date Submitted: 10/05/2021 01:17 AM

Average star voting: 3 ⭐ ( 54661 reviews)

Summary: Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower. In this work, an end-to-end deep learning framework based on convolutional neural network (CNN) is proposed for ECG signal processing and arrhythmia classification. In the framework, a transformer network is embedded in CNN to capture the temporal information of ECG signals and a new link constraint is introduced to the loss function to enhance the classification ability of the embedding vector. To evaluate the proposed method, extensive experiments based on real-world data were conducted. Experimental results show that the proposed model achieve better performance than most baselines. The experiment results also proved that the transformer network pays more attention to the temporal continuity of the data and captures the hidden deep features of the data well. The link constraint strengthens the constraint on the embedded features and effectively suppresses the effect of data imbalance on the results. In this paper, an end-to-end model is used to process ECG signal and classify arrhythmia. The model combine CNN and Transformer network to extract temporal information in ECG signal and is capable of performing arrhythmia classification with acceptable accuracy. The model can help cardiologists perform assisted diagnosis of heart disease and improve the efficiency of healthcare delivery.

Match with the search results: The transformer neural network receives an input sentence and converts it into two sequences: a sequence of word vector embeddings, and a sequence of positional ……. read more

Author: www.transformernet.com

Date Submitted: 06/30/2020 06:04 AM

Average star voting: 5 ⭐ ( 77406 reviews)

Summary:

Match with the search results: … you will discover the network architecture of the Transformer model. … How the Transformer architecture implements an encoder-decoder ……. read more

Author: towardsdatascience.com

Date Submitted: 05/16/2020 08:45 PM

Average star voting: 5 ⭐ ( 83108 reviews)

Summary: Discussions:
Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments)

Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese

Watch: MIT’s Deep Learning State of the Art lecture referencing this post

In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions.

The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter.

2020 Update: I’ve created a “Narrated Transformer” video which is a gentler approach to the topic:

A High-Level Look
Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.

Match with the search results: That versatile new hammer is a kind of artificial neural network — a network of nodes that “learn” how to do some task by training on existing ……. read more