Deep reinforcement learningTransfer learning[S U M M A R Y] Research on intelligent collision avoidance algorithms for unmanned surface vehicles (USVs) is challenging, as it aims to reduce casualties and enhance
Tensorflow: A system for large-scale machine learning Al-ShedivatM. et al. Continuous adaptation via meta-learning in nonstationary and competitive environments BengioY. et al. Curriculum learning BlumA. On-line algorithms in machine learning BussoC. et al. IEMOCAP: interactive emotional dyadic...
On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed ...
For more compute vision algorithms, please refer toOpenCVofficial site Deep Learning -- FromWiki: Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specifi...
Volodymyr Mnih, AdriaPuigdom enech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In ICML, 2016. Vijay R. Konda and John N. Tsitsiklis. Actor-critic algorithms. In NIPS, pages 10...
its ability to preserve intricate texture details while significantly reducing noise. However, while these methods have shown promise, they come with their own set of challenges. These include the necessity for manual parameter tuning and the reliance on computationally expensive optimization algorithms. ...
Thi Ngo PT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12:505–519 Article Google Scholar Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi...
To the best of our knowledge, this is the first study to integrate both hard data and soft data (e.g., head data) into hydrogeological modeling by coupling deep learning and data assimilation algorithms. In order to evaluate the influence of conditioned data (both hard and soft data) on ...
3.1. Deep-learning architecture U-Net is a popular CNN architecture for segmentation tasks, which is being increasingly adopted for mapping landslides from optical EO images (Ronneberger et al., 2015). This category of supervised learning algorithms is powerful and can automatically learn to identif...
The system need not perform a same learning algorithm to train a subsequent DNNs as learning algorithms used to train preceding DNNs, e.g., the first DNN. In other words, the system may apply different training algorithms to each of the DNNs in the sequence of DNNs. For example, one or...