Generative models as an emerging paradigm in the chemical sciences
DM Anstine, O Isayev - Journal of the American Chemical Society, 2023 - ACS Publications
Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …
compute properties for a vast number of candidates, eg, by discriminative modeling …
All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda
Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We
have created various computer-mediated virtual environments including social networks …
have created various computer-mediated virtual environments including social networks …
Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach
In a modern power system with an increasing proportion of renewable energy, wind power
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
A minimalist approach to offline reinforcement learning
S Fujimoto, SS Gu - Advances in neural information …, 2021 - proceedings.neurips.cc
Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …
Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
Multi-agent reinforcement learning is a sequence modeling problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …
performance and generalization capabilities in natural language process, vision and …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing
Mobile edge computing can effectively reduce service latency and improve service quality
by offloading computation-intensive tasks to the edges of wireless networks. Due to the …
by offloading computation-intensive tasks to the edges of wireless networks. Due to the …
Reinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …
help us find our favorite items to purchase, our friends on social networks, and our favorite …