Transfer learningBackground Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour ...
(which includes deep learning as well) and has multiple minima. Finding the global minimum of such a function is not a simple task as it is an NP-complete problem. The project will investigate existing optimisation methods as well as development of new ones for finding the local minima, the...
Deep Learning learning has recently become one of the most predominantly used techniques in the field of Machine Learning. Optimising these models, however, is very difficult and in order to scale the training to large datasets and model sizes practitioners use first-order optimisation methods. One...
The optimisation process yielded an estimate of parameters \(\alpha\) and \(\beta\) for the entire population. This process also allowed recovery of trial-by trial Q and p(action) values for each of the four actions. To calculate the accuracy of the reward-oriented model we chose the ...
Adam is a popular algorithm in the field of deep learning because it achieves good results fast. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. In the original paper, Adam was demonstrated empirically to show that converg...
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, many explanation methods have emerged. This work shows ...
In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel ...
After training, the performance of the models was evaluated to determine if they were suitably trained for use in the optimisation platform. The performance evaluation is summarised in this section. The model architecture and training process of fθ1 followed the implicit learning approach detailed ...
Optimisation methods in general. not limited to just Deep Learning 常用的优化方法。不仅限于深度学习 Neural Networks basic neural networks and multilayer perceptron 神经网络: 基本神经网络和多层感知器 Convolution Neural Networks: from basic to recent Research detailed explanation of CNN, various Loss ...
(FC1, FC2) to mask out with 70% probability and improve the performance17. The optimisation algorithm called AdaGrad (learning rate = 0.001), one of the stochastic gradient descent methods18, was used to train the network weights. We trained the model by using the GPU of GeForce GTX...