Deep neural networks (DNNs) models have the potential to provide new insights in the study of cognitive processes, such as human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, ...
For instance, as mentioned, machine learning is all about training an algorithm. But, to go further, in order to train an algorithm, you need a neural network—which is a set of algorithms inspired by biological neural networks. To connect this neural network to something they know, explain ...
Gradient-weighted class activation mapping (Grad-CAM) is an explainability technique that can be used to help understand the predictions made by a deep neural network [3]. Grad-CAM, a generalization of the CAM technique, determines the importance of each neuron in a network prediction by conside...
Embodiments of the present invention provide systems, methods, and computer storage media for providing factors that explain the generated results of a deep neural network (DNN). In embodiments, multiple machine learning models and a DNN are trained on a training dataset. A preliminary set of ...
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods Swish 1 人赞同了该文章 Abstract 本文主要进行交叉分析研究,对比了目前流行且先进的神经网络模型的可解释方法。同时利用问卷调查的方式,从用户的角度出发,参与者被要求在跨越图像、文本、音频和感觉域的应用程序中...
Scientists can use interpretable machine learning for a variety of applications, from identifying birds in images for wildlife surveys to analyzing mammograms. "I want to enhance the transparency fordeep learning, and I want a deep neural network to explain why something is the way it thinks it ...
The LIME technique approximates the behavior of a deep neural network using a simpler, more interpretable model, such as a regression tree. To map the importance of different parts of the input image, theimageLIMEfunction of performs the following steps. ...
The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three bench...
目录 论文解读之:High-frequencyComponentHelpsExplaintheGeneralizationofConvolutionalNeural...真的是噪声吗 九、总结 个人总结 论文解读之:High-frequencyComponentHelpsExplaintheGeneralizationofConvolutional Understanding deep learning requires rethinking generalization ...
However, their focus (Bahdanau et al., 2014) provide some means of inter- is mainly on using explanations to improve a model's pretability "for free" by examining the weight assigned predictions, and as such they propose first training a by the neural network to different regions in the ...