12 citations · 46 across the 18 of their papers we have counts for
11 papers · 1 filter
Generative Adversarial Networks Bridging Art and Machine Intelligence
Junhao Song, Yichao Zhang, Ziqian Bi +25
Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine i…
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
Binbin Hu, Zhicheng An, Zhengwei Wu +6
Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influen…
Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
Shiyu Wang, Zhixuan Chu, Yinbo Sun +7
Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with…
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
Rong Han, Wenbing Huang, Lingxiao Luo +5
Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for str…
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang +4
Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing…
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
Youshao Xiao, Shangchun Zhao, Zhenglei Zhou +5
Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in col…