deep learningechoemergency medicineemergency ultrasoundpoint‐of‐care ultrasoundLittle is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL ...
3, 4] used recurrentneural networks (RNNs) with long short-term memory cells [9]). Convolutional architectures yieldedgood results on word-level neural machine translation starting from [10] and later in [19]. These
# 计算天干地支获取随机幸运种子 [2]$ pip install randluck$ python>>> import randluck>>> random_seed = randluck.get_random_seed(strategy='bazi') 参考文献: [1]torch.manual.seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision, Dav...
最近在arxiv看到了一篇有意思的文章,标题是torch.manual seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision。不得不吐槽一下,最近很多论文标题都是"XX is all you need",似乎只要标题足够吸引眼球就能提高被录用的几率。但显然,这一trick是有效...
[1]torch.manual.seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision, David Picard,网页链接 [2]Random Luck 基于中国传统玄学自动获取随机种子,网页链接 点「在看」的人都变好看了哦!
Deep Reinforcement Learning with Double Q-Learning: [Paper][Code] Dueling Network Architectures for Deep Reinforcement Learning: [Paper][Code] Applications Image Recognition Deep Residual Learning for Image Recognition: [Paper][Code] Very Deep Convolutional Networks for Large-Scale Image Recognition: [Pa...
In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (up to 10^4 10^4 ) on CIFAR 10 and I also scan fewer seeds on Imagenet using pre-trained models to investig...
What are word embeddings?by Machine Learning Mastery. Other topics worth looking into: Attention mechanisms. These are a foundational component of the transformer architecture and also often add improvements to deep NLP models. Transformer architectures. This model architecture has recently taken the NLP...
Deep Reinforcement Learning with Double Q-Learning : [Paper][Code] Dueling Network Architectures for Deep Reinforcement Learning : [Paper][Code] Applications Image Recognition Deep Residual Learning for Image Recognition : [Paper][Code] Very Deep Convolutional Networks for Large-Scale Image Recognition ...
借鉴这篇论文的方法,韩松和朱俊彦团队将在 CVPR’20 发表了题为GAN Compression: Efficient Architectures for Interactive Conditional GANs的新成果。这是一篇关于如何压缩条件生成对抗网络(cGAN)算力消耗量过大的论文,提出了一种通用的压缩框架,以减少 cGAN 在运算过程中的推理时间和模型大小。