A convolutional neural network (CNN) is a category ofmachine learningmodel. Specifically, it is a type ofdeep learningalgorithm that is well suited to analyzing visual data. CNNs are commonly used to process image and video tasks. And, because CNNs are so effective at identifying objects, the...
注意力机制在RNN上的应用:Attention在输入输出序列建模已经司空见惯了,不依赖词和词之间的距离(CNN具有locality的Inductive Bias),但还只是以RNN的一个部分出现的。(主要用在有效地将编码器的输出传输到解码器作为输入,摘要里提到了) In this work we propose the Transformer, a model architecture eschewing(故意避...
An intelligent lossless network uses the AI-ready hardware architecture and iLossless algorithm — an AI-powered intelligent lossless algorithm — to achieve the maximum throughput and minimum latency without packet loss in AI, distributed storage, and HPC scenarios. This accelerates computing and ...
Variational autoencoders (VAEs)use innovations in neural network architecture and training processes and are often incorporated into image-generating applications. They consist of encoder and decoder networks, each of which may use a different underlying architecture, such as RNN, CNN, or transformer....
We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the...
Scale-Invariant Feature Transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images. This includes object recognition, robotic mapping and navigation, image stitching, and 3D modeling. Digital Outcrop Model (DOM) is a digital 3D representation of the out...
When the gradient isvanishingand is too small, it continues to become smaller, updating the weight parameters until they become insignificant, that is: zero (0). When that occurs, the algorithm is no longer learning. Explodinggradients occur when the gradient is too large, creating an unstable...
The decision on whether the proposed region contains an object or not is made in the last stage by using linear SVMs. 2.1. Limitations of R-CNN Even though R-CNN is a scalable detection algorithm that can achieve a certain precision, there are some disadvantages in its usage. First of ...
Model architecture Positional Encoding Batch norm 和 Layer norm 论文链接:arxiv.org/pdf/1706.0376 Transformer论文逐段精读【论文精读】_哔哩哔哩_bilibili Introduction 之前的sequence transduction models(序列转录模型,用于机器翻译,如将英语翻译成德语。一般包含一个encoder和一个decoder)都是基于RNN或CNN的结构。RNN...
We will inspect the results, without providing mathematical or other proofs. And results might vary using different data, activation functions, etc. 4.5.1. The CNN Architecture Figure 11: High level overview of the CNN architecture. The code for the CNN inside the GAN looks like this: num_...