首先,位置之间的空间依赖性仅依赖于历史交通的相似性,模型学习了静态空间依赖性。2)另一个局限性是,许多现有的研究忽略了长期周期性依赖的转移。交通数据具有很强的日周期性和周周期性,基于这种周期性的依赖性可以用于预测。然而,一个挑战是交通数据并不是严格的周期性数据。例如,工作日的高峰时间通常发生在下午晚些...
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold N... AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural ...
we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with...
Deep learning algorithms have been utilized to achieve excellent performance in pattern-recognition tasks, such as in image and vocal recognition. The ability to learn complex patterns in data has tremendous implications in the genomics world, where sequence motifs become learned features that can be ...
framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims ...
A deep learning framework for quality assessment and restoration in video endoscopy Author links open overlay panelSharib Ali a d e, Felix Zhou b d, Adam Bailey c d e, Barbara Braden c d e, James E. East c d e, Xin Lu b d e, Jens Rittscher a dShow more Add to Mendeley Share ...
Our approach is one of the pioneering work that proposes a deep learning framework with TFRs as input for solving the heart rate estimation from facial videos. In addition, we have developed a heart rate database, named the Pulse From Face (PFF), and used it along with the existing PURE ...
今天阅读了一篇论文,题目叫《DRN: A Deep Reinforcement Learning Framework for News Recommendation》。该论文便是深度强化学习和推荐系统的一个结合,也算是提供了一个利用强化学习来做推荐的完整的思路和方法吧。本文便是对文章中的内容的一个简单的介绍,希望对大家有所启发。
Deep Learning Approaches Deep belief network (DBN, Jia, 2016; Huang, 2014) Stacked autoencoder (SAE, LV, 2015; Chen, 2016) --(not to extract spatial and temporal features jointly) Spatial: CNN Temporal: RNN CLTFP (2016, Wu&Tan, LSTM+1D-CNN) ...
Direction-of-arrival (DOA) estimation problem is one of the most important tasks for array signal processing. Conventional methods are limited by either the computational complexity or the resolution. In this paper, a novel deep learning (DL) framework for super-resolution DOA estimation is develope...