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Found 4 papers, 0 papers with code
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator
Spiking neural network (SNN) is a brain-inspired model which has more spatio-temporal information processing capacity and computational energy efficiency.
Sub-GMN: The Neural Subgraph Matching Network Model
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
Balanced Order Batching with Task-Oriented Graph Clustering
In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem.
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba.
