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 ...
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,...
Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning Aparna Kumari, Riya Kakkar, Sudeep Tanwar, Deepak Garg, ... Amr Tolba Article 109031 select article Material and structural properties of recycled coarse aggregate concrete made with seawater and sea-sand: A...
Hyperledger Aries Cloud Agent Python (ACA-Py) is a foundation for building Verifiable Credential (VC) ecosystems. It operates in the second and third layers of theTrust Over IP framework (PDF)usingDIDComm messagingandHyperledger Ariesprotocols. The "cloud" in the name means that ACA-Py runs on...
Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is! Building Recommender Systems with Machine Learning and AI 2024 pdf epub mobi 电子书 Building Recommender Systems with Machine Learning and AI 2024 pdf epub mobi 电子书 ...
Deep Learning and Reinforcement Learning researcher building RL agents that meta-learn their own learning algorithm. Currently pursuing a PhD in Artificial Intelligence at IDSIA with Jürgen Schmidhuber.
one of us has taught deep learning to thousands and now works on making AI tooling and infrastructure easier to use. Despite our different experiences, we were struck by the consistent themes in the lessons we’ve learned, and we’re surprised that these insights aren’t more widely discussed...
Deep learning: As deep learning models have shown strong results in other areas of computing, we found researchers to have applied them to Android malware detection as well. Evidently, different architectures of deep learning have been tried. Yuan et al. (2014,2016) for example, tried Deep Bel...
In this paper, we introduce an improved method that is able to predict regularized building outline in a vector format within an end-to-end deep learning framework. The main idea of our framework is to learn to predict the location of key vertices of the buildings and connect them in ...