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Found 7 papers, 2 papers with code
Hierarchical Neural Constructive Solver for Real-world TSP Scenarios
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task.
Differentiable Cluster Graph Neural Network
Graph Neural Networks often struggle with long-range information propagation and in the presence of heterophilous neighborhoods.
Constrained Layout Generation with Factor Graphs
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process.
Joint Triplet Loss Learning for Next New POI Recommendation
Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences.
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem
This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location.
STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation.
