Deep learningCloud computingEdge computingFog computingIoTModelsIn recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, ...
In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that...
deep transfer learning 5.1. transfer learning the primary objective behind transfer learning is to apply the knowledge gained from addressing one problem to a new but related topic. pre-trained models are trained on one dataset and are applied to solve problems on another dataset. numerous pre-...
This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models). - vinits5/learning3d
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...
Zisserman, “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps,” pp. 1–8, 2013.[129] Y. N. Dauphin, H. De Vries, and Y. Bengio, “Equilibrated adaptive learning rates for non-convex optimization,” Adv. Neural Inf. Process. Syst., vol. 2015-...
[4] Tiancheng Zhao, Kaige Xie, and Maxine Eskenazi. Rethinking action spaces for reinforcement learning in end-to-end dialog agents with latent variable models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo...
[4] G. Jeong and H. Y. Kim,Improving fifinancial trading decisions using deepQ-learning: Predicting the number of shares, actionstrategies, and transfer learning, Expert Systems with Applications, 117 (2019), pp. 125–138. [5] K. Dabérius, E. Granat, and P. Karlsson,Deep execution-va...
2021-JMLR-Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks 2022-IJCAI-Recent Advances on Neural Network Pruning at Initialization 2021.6-Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better Papers [Pruning and...
The contemporaneous development in recent years of deep neural networks, hardware accelerators with large memory capacity and massive training datasets has advanced the state-of-the-art on tasks in fields such as computer vision and natural language processing. Today’s deep learning ...