This study also includes the discussion of numerous potential future options and limitation for 3D vision transformers.doi:10.1007/s11831-024-10108-4Gezawa, Abubakar SulaimanLiu, ChibiaoJunejo, Naveed Ur RehmanChiroma, HarunaSpringer NetherlandsArchives of Computational Methods in Engineering
Methods Hardware configuration All the results presented in this manuscript were generated using the Palmetto cluster, from Clemson University (palmetto.clemson.edu). A NVIDIA Tesla V100 was used as graphical processing unit (GPU) to train and fine-tune the deep learning model. The Palmetto computer...
Similar to existing flops calculation tools or methods, the DeepSpeed Flops Profiler measures the flops of the forward pass of a module and the flops of the backward pass is estimated as 2 times of that of the forward pass 告诉我们OpenAI的反向传播用前向的两倍时间估计Training cost是一定程度靠谱...
From the computational perspective, there is a rich line of work on learningword embeddingsbased onstatistical regularitiesfrom unlabeled corpora, following the well-established Distributional Hypothesis[41],[35]. The first type of distributional word representations relied on count-based methods, initially...
1 Comparison of the proposed TransPHLA method with 14 existing methods on HPV vaccine data with threshold 500 nM. The number of true positive and false negative are described, and the sum of true positive and false negative represents the number of predictable peptide-HLA-I binders. Source ...
4.7. Comparison with State-of-the-Art Methods 在表6 中,我们的 TransReID 在六个基准(包括 person ReID、occluded ReID 和 vehicle ReID)上与最先进的方法进行了比较。 Person ReID.在 MSMT17 和 DukeMTMC-reID 上,TransReID* (DeiT-B/16) 相比之前的最先进方法有很大优势(+5.5%/+2.1% mAP)。在 Marke...
theposterior involves a super-exponential growth of the number ofhypotheses over time, forcing state-of-the-art methods to resort toapproximations for remaining tractable, which can impact theirperformance in complex scenarios. Model-free methods based ondeep-learning provide an attractive alternative, ...
Convolutional neural networks have made great breakthrough in recent remote sensing image super-resolution tasks. Most of these methods adopt upsampling layers at the end of the models to perform enlargement, which ignores feature extraction in the high-dimension space and thus limits super-resolution...
Experimental results have also demonstrated that pre-trained models outperform previous methods, particularly in property prediction33, suggesting remarkable improvement through pre-training. Inspired by this, we propose the Uni-MOF framework as a multi-purpose solution for predicting gas adsorption of ...
Recently, the scenes in large high-resolution remote sensing (HRRS) datasets have been classified using convolutional neural network (CNN)-based methods. Such methods are well-suited for spatial feature extraction and can classify images with relatively