MACHINE learningBIG dataAs an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions ...
This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models). - vinits5/learning3d
2021-TPDS-The Deep Learning Compiler: A Comprehensive Survey 2021-JMLR-Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks 2021.6-Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better 2022-IJCAI-Recent Advances ...
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models.Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have ... V Yadav,S Bethard - International Conference on Computati...
We analyze state-of-the-art deep learning models for three tasks: question answer- ing on (1) images, (2) tables, and (3) pas- sages of text. Using the notion of at- tribution (word importance), we find that these deep networks often ign... PK Mudrakarta,A Taly,M Sundararajan,....
and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) an...
Prior to using the cryo-EM density map to train deep learning models, it is generally necessary to normalize and standardize the data to make them suitable for deep learning as shown by Cascaded-CNN and DeepTracer, which perform data grid resampling, density value normalization, and grid divisio...
1.背景介绍 在过去的几年里,自然语言处理(NLP)技术取得了显著的进展,这主要归功于深度学习和大规模数据集的应用。在这个过程中,Transformer模型在NLP领域的表现...
in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning...
[19] proposed a new approach based on Deep Neural Networks and transfer learning, since DNN models are pretrained with simulated data from analytical stability models. The aim was to reduce the differences between the real measurements and the model output due to the cutting forces and the ...