Search Results for author:
Found 25 papers, 10 papers with code
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.
LLMLight: Large Language Models as Traffic Signal Control Agents
Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion.
Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience
Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change.
Applying Large Language Models to Power Systems: Potential Security Threats
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics.
3D Molecular Geometry Analysis with 2D Graphs
Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs.
Learning Sparsity of Representations with Discrete Latent Variables
The resulting sparsity of a representation is not fixed, but fits to the observation itself under the pre-defined restriction.
Uncertainty Propagation in Node Classification
To address these issues, we propose a Bayesian uncertainty propagation (BUP) method, which embeds GNNs in a Bayesian modeling framework, and models predictive uncertainty of node classification with Bayesian confidence of predictive probability and uncertainty of messages.
Multi-objective Generative Design of Three-Dimensional Composite Materials
Composite materials with 3D architectures are desirable in a variety of applications for the capability of tailoring their properties to meet multiple functional requirements.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.
A Human-Centric Assessment Framework for AI
Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.
Self-Supervised Representation Learning via Latent Graph Prediction
Self-supervised learning (SSL) of graph neural networks is emerging as a promising way of leveraging unlabeled data.
Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network
However, the existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way and tends to cause much delay.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.
Peer-to-Peer Energy Cooperation in Building Community over A Lossy Network
Energy management of buildings is of vital importance for the urban low-carbon transition.
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
Compared with AUROC, AUPRC is a more appropriate metric for highly imbalanced datasets.
Real-time Operation Optimization of Microgrids with Battery Energy Storage System: A Tube-based Model Predictive Control Approach
In this paper, a tube-based model predictive control (MPC) approach is innovatively proposed in accommodating the real-time energy management of microgrids with battery ESS.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
Robust Online Time Series Prediction with Recurrent Neural Networks
Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare.
