[54]Aladin Virmaux and Kevin Scaman. Lipschitz regularity of deep neural networks: analysis and efficient estimation. InNeurIPS, pages 3839–3848, 2018. [55]Nikil Wale, Ian A Watson, and George Karypis. Comparison of descriptor spaces for chemical compound retrieval and classification.Knowledge and...
参考内容如下: Toneva M, Sordoni A, Combes R T, et al. An empirical study of example forgetting during deep neural network learning[J]. arXiv preprint arXiv:1812.05159, 2018. PR-261: Empirical Study of Forgetting Events during Deep Neural Network Learning https://qdata.github.io/deep2Read...
2.2 Neural networks and deep learning Describing today's groundbreaking AI achievements, we have to realize that the underlying powerful deep learning approaches are based on neural network research conducted for many decades, motivated by the knowledge accumulated on the operation and functions of biolo...
Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data. However, implementing a deep neural network in embedded systems is a challenging task, because a typical deep neural network, such as a Deep Belief Network using 128x128 images...
of the variables of the data, since ANN are nonparametric in nature. ANN require a much smaller data set than that required for conventional regression analysis for capturing the nonlinear relationships between the input and output parameters. Even with a small training data set, the network ...
当将10个不同的种子随机分成两组5时,这两组中遗忘事件的累积数量显示出97.6%的高相关性。我们还对100个种子进行了原始实验,以设计每个例子中遗忘事件平均数(超过5个种子)的95%置信区间。最少被遗忘的例子的置信区间很紧,确认了具有少量遗忘事件的例子可以自信地提升准确率。
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DeepACSON automated segmentation of white matter in 3D electron microscopy Article Open access 10 February 2021 Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets Article 29 February 2024 Dense cellular segmentation for EM using 2D–3D neural network ensembl...
A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object recognition, including image classification, detection, and segmentation. CNNs are employed in a variety of practical scenarios, such as aut...
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperfor...