This paper improves nearest neighbor search by learning optimal routing functions.
problem Local minima issues in greedy routing on similarity graphs.
method Learn routing function that considers global graph structure.
result Significant improvement in search performance via learning.
New method tackles parcel routing with AI.
problem Routing parcels efficiently through a network of hubs.
method Combines graph neural networks with model-free RL.
result Extracts small feature graphs from the environment state.
Sparse RSP routing improves graph exploration and classification.
problem Optimal randomized routing and distance measures on weighted graphs.
method Tsallis divergence regularization for sparse RSP.
result Sparse random walk converges to least-cost graph as temperature decreases.
StackSeq2Seq improves route finding on graphs using deep neural networks.
problem Finding the shortest path between graph nodes.
method Dual Encoder Seq2Seq architecture, context vector, homotopy continuation.
result Increased accuracy in learning shortest routes on graphs.
Proposes a graph neural network for efficient multi-agent routing.
problem Routing multiple agents in complex, sparsely connected graphs with dynamic traffic.
method Value Iteration Network based on graph neural networks for online coordination.
result Significantly outperforms traditional methods in terms of cost and runtime.
We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar…
This paper introduces STAP to measure DEX efficiency and shows better routing algorithms increase DEX performance and stakeholder benefits.
problem Measuring and improving the efficiency of decentralized exchanges (DEXs).
method Introduces STAP as a measure of DEX efficiency and compares two routing algorithms.
result Better routing algorithms improve DEX efficiency and stakeholder benefits.
Polestar optimizes public transportation routes for efficiency and user satisfaction.
problem Difficulty in finding optimal public transportation routes due to complex networks and dynamic situations.
method Developed a Public Transportation Graph (PTG) and a route search algorithm with station binding and ranking modules.
result Demonstrated superior efficiency and user satisfaction compared to existing systems.
LG algorithm finds profitable trading paths in decentralized exchanges.
problem Identifying optimal trading paths in decentralized exchanges.
method Line-graph-based algorithm (LG) for efficient route discovery.
result LG consistently identifies more profitable paths than DFS with comparable costs.
Graph Informer improves molecular prediction tasks with expressive route-based attention.
problem Challenges in applying machine learning to molecular graphs due to information bottlenecks.
method Introduces a multi-attention mechanism that attends to nodes several steps away in molecular graphs.
result Improves 13C NMR spectra prediction with MAE of 1.35 ppm and drug bioactivity/toxicity prediction.
Enhanced route planning with probabilistic prediction and uncertainty sets.
problem Improving route planning reliability under uncertainty.
method CQR-GAE model integrating conformal prediction and uncertainty sets.
result Significantly outperforms baseline methods in real-world traffic scenarios.
A decentralized routing framework for lunar exploration robots.
problem Routing data in intermittent connectivity lunar networks.
method Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL).
result Higher delivery rates, no duplications, fewer packet losses.
Method estimates travel times on urban roads using Uber data.
problem Estimating travel times on urban roads where data is scarce.
method Graph representation, trip sampling, least-squares optimization.
result Estimates travel times on arterial roads using aggregated Uber data.
In this work we study the degree distribution, the maximum vertex and edge flow in non-uniform random Delaunay triangulations when geodesic routing is used. We also investigate the vertex and edge flow in Erdös-Renyi random graphs, geometric random graphs, expanders and random k-regular graphs. Moreover we show that …
IPGDN learns disentangled node representations in graphs.
problem Learning disentangled node representations in graph convolutional networks (GCNs).
method IPGDN uses neighborhood routing mechanism and HSIC to enforce independence among latent representations.
result IPGDN outperforms state-of-the-arts in graph classification, clustering, and visualization.
A new model improves the performance of knowledge distillation in GNNs.
problem Inconsistent performance of existing knowledge distillation techniques in GNNs.
method Proposes a new model, Routing-by-Memory (RbM), a form of Mixture-of-Experts (MoE), to address performance concerns.
result Demonstrates experimentally that RbM achieves considerably more consistent performance across multiple datasets.
Deep learning solves EV routing with time windows for EV fleets.
problem Electric vehicle routing with time windows for logistics.
method End-to-end deep reinforcement learning with attention model and graph embedding.
result Proposed model efficiently solves large EVRPTW instances.
The paper introduces a new method to improve model generalization by routing model copies through permutations.
problem Improving model generalization in machine learning.
method The method replicates a model \(M\) times and rewire the contexts in which local learning messages are computed using permutations.
result The method improves generalization by structured message sharing rather than coupling parameters.
Boosts share routing for multi-task learning with flexible sparse connections.
problem Designing suitable sharing mechanisms among multiple tasks in multi-task learning.
method Proposes MTNAS framework to modularize sharing into sub-networks with sparse connections and gating.
result Demonstrates consistent improvement over single-task and typical multi-task methods while maintaining efficiency.
STAD improves travel time estimation by learning from real traffic data.
problem Travel time estimation using GPS traces is inaccurate and requires offline optimization.
method STAD uses machine learning and real-time trip data to adjust travel time estimates.
result STAD reduces median absolute errors by 14% in Doha and New York City, and 29% in Porto.
SubGNN tackles subgraph prediction challenges in graphs.
problem Subgraphs in graphs are challenging to predict due to their internal topology and external connectivity.
method SubGNN introduces a novel subgraph routing mechanism to learn disentangled subgraph representations.
result SubGNN achieves considerable performance gains on subgraph classification tasks, outperforming strong baseline methods.
G5 universal GRAPH-BERT learns graph representations across different datasets.
problem Learning graph representations across diverse graph datasets with distinct input and output configurations.
method G5 introduces a pluggable model architecture with input and output components for each graph data source, connected via a unified layer and fusion layer.
result G5 removes obstacles for cross-graph representation learning and transfer, even for sparse data.
Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the net…
This paper improves route choice models by incorporating contextual factors.
problem Existing route choice models lack consideration of dynamic contextual conditions.
method Knowledge distillation from Stated Choice Experiments in Immersive Virtual Environment.
result High-fidelity route choice models with increased predictive power.
Capsule networks improve performance on image classification tasks with fewer parameters.
problem Improving performance of capsule networks with fewer parameters.
method Inverted dot-product attention routing, Layer Normalization, concurrent iterative routing.
result Improves performance on benchmark datasets CIFAR-10 and CIFAR-100, and performs at-par with ResNet-18.
Deep RL solves global routing problems more efficiently.
problem Challenging problem in electronic circuit design and other systems.
method Deep reinforcement learning for conjoint optimization.
result Deep RL outperforms A* method in routing solutions.
Capsules learn from expert neurons using dynamic routing.
problem Training complex multidimensional neural networks.
method Formulated capsule networks as a product of expert neurons with dynamic routing.
result Capsule networks can generate realistic images.
Graph Neural Networks solve the decision variant of TSP with high accuracy.
problem Decision variant of the Traveling Salesperson Problem (TSP).
method Graph Neural Networks (GNNs) learn to solve TSP with minimal supervision.
result Graph Neural Networks achieve 80% accuracy in solving TSP with small cost deviations.
JAMPR learns to solve complex VRP with time windows.
problem Vehicle routing problems with time windows and vehicle capacities.
method Joint attention to construct multiple routes concurrently.
result JAMPR outperforms existing models on different problem sizes.
A large GPS dataset reveals that a single route often covers 60% of travel observations.
problem Limited insights from small GPS datasets on route choice behavior.
method Evaluation of path generation algorithms including link penalty, link elimination, simulation, and via-node methods.
result Modified link penalty method achieves 97% coverage, significantly higher than previous studies.
Capsule networks can only represent symmetric functions due to routing limitations.
problem Capsule networks' expressivity is limited to symmetric functions.
method Proved and empirically demonstrated that EM-routing and routing-by-agreement prevent capsule networks from distinguishing inputs and their negative counterpart.
result Capsule networks are not universal approximators due to the limitation of expressivity.
A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.
problem Solving detailed routing in integrated circuits while adhering to complex design rules.
method Attention-based reinforcement learning applied to track-assignment detailed routing.
result The attention router achieves over 100x acceleration compared to a genetic router without sacrificing solution quality.
New dg-algebras generalize Brauer graph algebras, with applications to stability conditions and quadratic differentials.
problem Generalizing Brauer graph algebras to new dg-algebras.
method Derived categories, mixed-angulations of surfaces, stability conditions, and quadratic differentials.
result Spaces of stability conditions on derived categories of these algebras are described in terms of spaces of quadratic differentials.
Predicts vessel destinations using AIS data and nearest neighbor search.
problem Accurately predict the destination ports and arrival times of vessel trips.
method Partitioned training routes by destination port, use nearest neighbor search, and incorporate improvements like avoiding frequent port changes and automating parameter tuning.
result Significant improvements in prediction accuracy compared to baseline methods.
Project learns to model Capsule Networks' routing procedures for better expressiveness.
problem Limited expressiveness of Capsule Networks' inner routing procedures.
method Proposes two ways to learn the routing procedure as a network parameter.
result Improved expressiveness of Capsule Networks through learned routing procedures.
Caps2NE learns node embeddings in graphs using capsule layers.
problem Learning low-dimensional node embeddings in graph data.
method Caps2NE uses a two-capsule layer architecture with a routing process to aggregate neighbors' features.
result Caps2NE achieves state-of-the-art performance on node classification tasks.
A new approach integrates inventory prediction and routing optimization for better supply chain management.
problem Optimizing efficient route selection in supply chain management with uncertain inventory demand.
method Decision-focused learning approach using neural networks to directly integrate inventory prediction and routing optimization.
result Direct integration of inventory prediction and routing optimization leads to better supply chain decisions.
Unified routing and arbitrage with concave continuation.
problem Combining routing and arbitrage in financial markets.
method Extending AMM trade functions to negative inputs via concave continuation.
result Unified approach unifies routing and arbitrage.
Sparse routing networks with co-training prevent catastrophic forgetting in continual learning.
problem Catastrophic forgetting in neural networks trained on a sequence of tasks.
method Sparse routing networks with co-training to minimize interference between dissimilar tasks.
result Sparse routing networks with co-training outperform densely connected networks on benchmarks.
Neural LNS improves vehicle routing performance.
problem Optimizing vehicle routes with constraints.
method Integrates deep learning with large neighborhood search.
result Significantly outperforms existing methods on CVRP.
SafeRNet uses IoT and cloud computing to provide real-time safe routes.
problem High traffic fatality rates despite advanced technology.
method Bayesian network for safe route modeling.
result Demonstrated effectiveness with real traffic data.
Predicts routing congestion in FPGA designs using machine learning.
problem Routing congestion estimation is absent or inaccurate in HLS design methods.
method Machine learning to predict routing congestion in HLS.
result Accurately estimates routing congestion with errors of 6.71% and 10.05%.
Capsule networks improve at detecting changes in compositionality with routing.
problem Capsule networks struggle with detecting changes in compositionality.
method Introduced a loss function based on routing entropy to improve compositionality.
result Capsule networks with the new loss function better detect changes in compositionality.
This study develops methods to coordinate travel routes to reduce congestion.
problem Coordination of travel routes to reduce urban traffic congestion.
method Developed mathematical approaches to quantify coordination potential and adaptive centroid-based clustering algorithm (ACCA).
result ACCA efficiently forms proper coordination groups for CB-CRM, improving efficiency with minimal performance loss.
Optimizes exploration in networks by interpolating between random and deterministic paths.
problem Balancing exploitation and exploration in network routing with constraints.
method Developed a constrained randomized shortest-paths framework using Lagrangian duality and iterative procedures.
result Optimal routing policy that interpolates between random and deterministic paths while satisfying constraints.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
Study finds average 2.02 bps loss in automated market maker routing.
problem Measuring sub-optimality in automated market maker routing.
method Three reproducible optimal benchmarks: SCO, FVO, G-FVO; bisection-based algorithm for optimal routing.
result Average 2.02 bps loss per trade, \$24 million total loss.
We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive model will be used for a subsequent task, thus combining machine learning with t…