This work presents kCNN-LSTM, a deep learning framework that operates on the energy consumption data recorded at predefined intervals to provide accurate building energy consumption forecasts. kCNN-LSTM employs (i) kmeans clustering – to perform cluster analysis to understand the energy consumption ...
We use MXNet as an example of deep learning frameworks that can run on Azure. MXNet is an open-source framework for deep neural networks with support for multiple languages and platforms that aims to provide both execution efficiency and design flexibility...
Developed by NVIDIA, fVDB is a deep-learning framework for sparse, large-scale, high-performance spatial intelligence. It builds NVIDIA-accelerated AI operators on top of OpenVDB to enable digital twins at reality scale, neural radiance fields, 3D generative AI, and more. A...
NVIDIA Modulus is an open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art SciML methods for AI4science and engineering. Modulus provides utilities and optimized pipelines to develop AI models that combine physics knowledge with data,...
Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep...
We propose a multi-spectral, multi-view, and multi-task deep network (called M3Net) for building height estimation, where ZY-3 multi-spectral and multi-view images are fused in a multi-task learning framework. A random forest (RF) method using multi-source features is also carried out for...
When using a deep learning framework, implementing the forward pass is sufficient to build systems that achieve great performance. The rest of this notebook is optional, and will not be graded. 1|63 - Backpropagation in recurrent neural networks (OPTIONAL / UNGRADED) In modern deep learning ...
Deep-Learning for Lod1 Building Reconstruction from Airborne Lidar Data A three-dimensional building model is an important geospatial information for a smart city. The objective of this study is to reconstruct OGC CityGML LOD1 ... TA Teo - Igarss IEEE International Geoscience & Remote Sensing Sym...
A novel level set framework for LOD2 building modeling, international conference on image processing, pp. 1781–1784. Google Scholar Kendall et al., 2017 Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., 2017. End-to-End Learning of Geometry and Context for Deep Stereo Regression,...
In the end, I may just write my own deep learning framework for Windows and Java. Contributor aselle commented Jun 16, 2017 • edited I empathize that you are super frustrated, but as a developer, you must know that we don't have unlimited time to support everything ourself. You ...