Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs论文阅读笔记 链接:论文 这篇文章是商汤科技提出的,主要内容就是应用快速算法winograd以及FFT,对CNN进行快速计算,并在FPGA上进行验证。 引言 文章认为DSP是消耗最多的资源,如果能减少乘法计算次数,是能够提高DSP效率的。 FFT/winograd是将输入特征图...
which are usually used as the first layer of CNNs and as an effective alternative to the pooling layers for downsampling.In this paper, we first introduce rearrangement-and sampling-based methods for applying FFT-based fast algorithms to strided convolutions, and the arithmetic complexities of th...
across tasks for the original BPNet model (average Matthews correlation coefficient (MCC) of 0.665), which we could improve by increasing its size to 28 million parameters (average MCC of 0.683), confirming that directly supervised convolutional architectures perform very well for genomics tasks (...
For example, our model has most likely learned to associated algorithms with certain applications such as CNNs used for computer vision or transformers used for NLP projects. However, these algorithms are not being applied beyond their initial use cases. We’d need ensure that our model learns ...
3 utilized six machine learning algorithms to predict the factor of safety (FOS) for slope stability, using a dataset of 327 slope examples from Iran. The GPR model had the highest level of accuracy, as evidenced by an R2 value of 0.8139, an RMSE value of 0.160893, and a MAPE value of...
The main objective of this research is to assess the ability of the CCD models as well as few-shot learning algorithms for unseen programming problems and new languages (i.e., the model is not trained on these problems/languages).
1. Comparison of datasets for evaluating 6-DOF tracking algorithms. Typical RGB (top) and depth (bottom) frames for (a) the synthetic dataset of Choi and Chris- tensen [4], (b) the real dataset of Garon and Lalonde [3], and (c) ours. Compared to the previous work, our dataset ...
(average MCC of 0.683), confirming that directly supervised convolutional architectures perform very well for genomics tasks (Fig.2a,b). We next evaluated how probing and fine-tuning of the NT models compare with these supervised baseline models on our benchmark datasets. We considered the models...
Importantly, as accurate and precise as most AI algorithms are; owing to numerous research focus and resources dedicated to them of recent, their applications to digital forensics require significant cautions, and consideration for domain-specific intricacies. Clearly, the results of a business-oriented...
Protein docking model evaluation by 3D deep convolutional neural networks. Bioinformatics 36, 2113–2118 (2020). Article Google Scholar Renaud, N. et al. DeepRank: a deep learning framework for data mining 3D protein-protein interfaces. Nat. Commun. 12, 7068 (2021). Mohseni Behbahani, Y.,...