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cs.LG2025
ACA-Net: Future Graph Learning for Logistical Demand-Supply Forecasting
Jiacheng Shi, Haibin Wei, Jiang Wang +3
Logistical demand-supply forecasting that evaluates the alignment between projected supply and anticipated demand, is essential for the efficiency and quality of on-demand food del…
cs.LG2025
MindSpeed RL: Distributed Dataflow for Scalable and Efficient RL Training on Ascend NPU Cluster
Laingjun Feng, Chenyi Pan, Xinjie Guo +11
Reinforcement learning (RL) is a paradigm increasingly used to align large language models. Popular RL algorithms utilize multiple workers and can be modeled as a graph, where each…
cs.LG2024
STTM: A New Approach Based Spatial-Temporal Transformer And Memory Network For Real-time Pressure Signal In On-demand Food Delivery
Jiang Wang, Haibin Wei, Xiaowei Xu +4
On-demand Food Delivery (OFD) services have become very common around the world. For example, on the Ele.me platform, users place more than 15 million food orders every day. Predic…