The Advances and Challenges of Deep Learning Application in Biological Big Data ProcessingDeep learningmachine learningbig databioinformaticsbiological imageBackground: Bioinformatics research comes into an era of big data. Mining potential value in biological big data for scientific research and health care...
We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.Similar content being viewed by others Reusability report: Deep ...
software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. Similar content being viewed by others R...
Switch-based active deep dyna-q: Efficient adaptive planning for task-completion dialogue policy learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 7289–7296, 2019. [20] Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, and Yun-Nung Chen. ...
Recent Advancement and Challenges in Deep Learning, Big Data in Bioinformatics Chapter © 2022 References Haugeland J (1985) Artificial intelligence: the very idea Google Scholar Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern...
The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research...
Stanford Seminar - Recent Advances in Deep Learning斯坦福研讨会 - 深度学习的最新进展 "Recent advances in deep learning" - Oriol Vinyals of Google Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Fo
Challenges and Future Directions 虽然深度学习模型在医学图像分析方面取得了巨大的成功,但小规模医疗数据集仍然是该领域的主要瓶颈。 迁移学习(Transfer learning) 顾名思义就是把已训练好的模型(预训练模型)参数迁移到新的模型来帮助新模型训练。考虑到大部分数据或任务都是存在相关性的,所以通过迁移学习我们可以将已...
Deep learning has become the mainstream technology in computer vision, and it has received extensive research interest in developing new medical image processing algorithms to support disease detection and diagnosis. As compared to conventional machine learning technologies, the major advantage of deep lear...
and the promised gains of VSR systems, have motivated extensive efforts on developing the lip reading technology. This paper provides a comprehensive survey of the state-of-the-art deep learning based VSR research with a focus on data challenges, task-specific complications, and the corresponding ...