This is because the goal of XAI is to design machine learning models with the capacity to be explained. Over the past decade, the field of XAI has grown dramatically [33,34]. This has led to the creation of a multitude of domain-dependent and context-specific methods for interpreting ML ...
Depending on the data that needs to be explained, explanation methods can be graph-based, image-based, text-based, and tabular-based, while the agnosticity indicates if the explanation method is applicable to explain any type of ML model or if it can explain only a specific type. Some of...
Later, a sigmoid function is applied to it. That will make ft a number between 0 and 1. This ft is later multiplied with the cell state of the previous timestamp, as shown below. Input Gate Let’s take another example. “Bob knows swimming. He told me over the phone that he had ...
As explained in this article, an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. In our case, the "many to one"...
As it is well known, PyTorch provides a LSTM class to build multilayer long-short term memory neural networks which is based on LSTMCells. In this blog, it’s going to be explained how to build such a neural net by hand by only using LSTMCells with a practical example....
《Generating Sequences With Recurrent Neural Networks》(这个有对应源码,虽然实例用法是错的,自己用的时候还得改代码,主要是摘出一些来用,供参考)然后呢,可以开始编码了。除了前面提到的theano教程还有一些论文的开源代码,到github上搜就好了。顺便安利一下theano,theano的自动求导和GPU透明对新手以及学术界研究者来说...
master examples UNet resnet50 styletransfer variational_autoencoder .gitkeep ImageNet Pretrained Network (VGG_S).ipynb Saliency Maps and Guided Backpropagation.ipynb Using a Caffe Pretrained Network - CIFAR10.ipynb Video features with C3D.ipynb ...
1. The different components are explained in more detail in Fig. 2. Fig. 1 The proposed model. A reduced ConvNext-tiny is used as the encoder. The information between the encoded frames is fused using a bidirectional ConvLSTM. The decoder is inspired by the UNet. \(F\) is the number ...
As explained in [11], the main drawback of traditional FC-LSTM was its usage of full connections in the input-to-state and state-to-state transitions, which resulted in the neglect of spatial information. To retain such important information, ConvLSTM, proposed by Shi et al. [11], replace...
(including prefixes, suffixes, and stems), among others. Defining such features can be a labor-intensive and time-consuming. This is why many practitioners opt for a bidirectional LSTM model (I have explained LSTM inthis articlein case you want to refresh your knowledge) that can compute ...