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Found 7 papers, 2 papers with code
Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
Deep Learning-Based Decoding of Constrained Sequence Codes
Different from conventional decoding where the received sequence is processed bit-by-bit, we propose using CNNs to perform one-shot batch-processing of variable-length CS codes such that an entire batch is decoded at once, which improves the system throughput.
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.
Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
To identify and fit geometric primitives (e. g., planes, spheres, cylinders, cones) in a noisy point cloud is a challenging yet beneficial task for fields such as robotics and reverse engineering.
Deep Learning-Based Decoding for Constrained Sequence Codes
Constrained sequence codes have been widely used in modern communication and data storage systems.
