www.coursera.org/learn/convolutional-neural-networks 昨天来自Deeplearning.AI的一封邮件告诉我说他们更新了深度学习第四课CNN和第五课NLP(四月末发布),点进去看发现确实加了对近几年出现网络的教学(U-Net、EfficientNet等),然后也有粉丝私信我要我搬,行吧,光处理成双栏字幕就花了我好久时间。人工智能技术探讨群:...
Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical ...
《Deep Neural Networks for YouTube Recommendation》译读 《Deep Neural Networks for YouTube Recommendation》译读 摘要 youtube 代表了目前规模最大、最复杂的工业推荐系统之一。在这篇文章里,我们从系统的角度上重点讲述深度学习带来的巨大效果提升。根据经典的信息检索二分法,本文分为2阶段:首先,我们详细描述了...
The current leading approach for object detection is the Regions with Convolutional Neural Networks (R-CNN) method by Girshick et al. [6]. R-CNN decomposes the overall detection problem into two subproblems: utilizing low-level cues such as color and texture in order to generate object location...
目前，目标检测的领先方法是Girshick等人 [6] 提出的Regions with Convolutional Neural Networks (R-CNN)...
Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous applications. Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on d...
在硬件、数据集、模型、算法等方面提升目标分类和识别能力, Network in network利用1*1卷积层来增加深度。 三、工作 Gabor filters,Inception的使用,使用1*1卷积的目的有降低维度、增加深度、宽度,减少参数。 Regions with Convolutional Neural Networks (R-CNN)的物体候选框(bounding box)的精度。
In convolutional neural networks (CNN), 2D convolutions are the most frequently used convolutional layer. MobileNet is a CNN architecture that is much faster as well as a smaller model that makes use…
Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. Advances in Neural Information Processing Systems (NIPS 2016) Vol. 29, 3844-3852 (Curran Associates, Inc., 2016). Hamilton, W., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. ...
Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolut...