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.
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.
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.
Bayesian classifier predicts journey routes using Markov chains.
problem Predicting the route of ongoing journeys.
method Modeling journey patterns as stochastic processes and updating posterior probabilities with Markov chains.
result High accuracy in route predictions demonstrated on synthetic data.
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.
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%.
Framework optimizes transit routes based on crowd movements using demand prediction and supply optimization.
problem Dynamic optimization of transit routes in areas of crowd movements.
method Combines demand prediction (Quantile Regression) and supply optimization (Linear Programming) to dynamically redesign routes.
result Framework often obtains optimal solutions and outperforms conventional methods.
Neural network ensembles predict design rule violations from early stages of IC design.
problem Predicting design rule violations from placement and global routing stages in IC design.
method Proposes a framework using neural network ensembles with soft voting and PCA-based subset selection.
result Significant improvement in model performance compared to baseline, including better performance than random forest.
Predicts destinations and routes from partial trajectory data.
problem Predicting destinations and routes from partial trajectory data for applications like parking suggestions and ride-sharing.
method Three-step procedure: k-d tree-based space discretization, recurrent neural network for destination prediction, and route calculation.
result Best models predict destinations with a mean error of 1.3 km and 1.43 km.
The paper predicts travel times using tree-based ensembles.
problem Predicting travel times between urban points over short and long horizons.
method Tree-based ensemble methods trained on taxi trip records with additional features from weather and routing data.
result Adding routing data improves model performance and short-term predictions require less data.
Generative model predicts ETA for bus routes using local data.
problem Accurate ETA prediction for public transit, especially buses, in cities with limited data.
method Generative deep learning model trained on local bus route data.
result Model updates ETA in real-time based on current trip information.
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 improves dynamic PT fleet optimization under noisy demand predictions.
problem Accurately predicting dynamic public transport demand for effective fleet management.
method Experimental case study in Copenhagen, using linear programming to optimize fleets.
result Optimized fleet performance is mainly affected by noise distribution skew and large errors.
CARROT optimizes LLM routing by choosing the cheapest and most accurate model.
problem Optimizing query routing to the most cost-effective LLM for a given task.
method CARROT uses cost and performance estimates to select the best LLM for each query.
result CARROT is minimax optimal, selecting the best LLM for any query.
DPDP combines neural heuristics with DP for vehicle routing problems.
problem Vehicle routing problems with large scale.
method Deep Policy Dynamic Programming (DPDP) that uses a neural network policy to prioritize and restrict the DP state space.
result DPDP improves upon classical DP algorithms and outperforms neural approaches for TSP, VRP, and TSPTW.
Unified view of context-adaptive inference across statistics, meta-learning, and foundation models.
problem Adapting predictive systems to specific situations.
method Unified framework for context-adaptive inference, bridging statistics, meta-learning, and foundation models.
result Explicit and implicit approaches are mathematically equivalent under certain conditions.
A hierarchical routing mixture of experts model for complex regression tasks.
problem Complex data distribution in regression tasks.
method Binary tree-structured hierarchical routing mixture of experts (HRME) model with classifiers and simple regression models.
result Effective prediction with simple leaf experts in multimodal data.
A dynamic ResNet model learns different routes for images from different classes.
problem Fixed structure in ResNet-like architectures limits their adaptability to diverse inputs.
method Develops a ResNet-based model that dynamically selects Computational Units for each input image.
result Achieves better results on CIFAR-10 test set compared to the original ResNet-38 architecture.
Tutorial on recursive models for predicting path choices.
problem Modeling path choice behavior of network users.
method Recursive discrete choice models.
result Advantages of recursive models over path-based models.
MER-SDN uses machine learning to optimize energy efficiency in SDN networks.
problem Energy efficiency in SDN networks is challenging and impacts both economics and environment.
method Feature extraction, training, and testing phases of a machine learning framework.
result Achieves up to 25X speedup in predicting optimal parameters for energy efficient routing.
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.
DRNets dynamically route instances to efficient transformations.
problem High inference costs due to static model capacity.
method Dynamic Routing Networks (DRNets) with RouterNets for branch selection.
result DRNets reduce inference costs with comparable performance.
Deep CapsNet improves sign language recognition from wearable IMUs.
problem Continuous recognition of sign language from wearable devices.
method Custom CapsNet architecture using deep capsule networks and game theory.
result Improved accuracy of 94% and 92.50% for 3 and 5 routings respectively, compared to 87.99% for CNN.
End-to-end framework learns LLM routing from observational data.
problem Compounding errors in decoupled approaches and reliance on full-feedback data.
method Causal end-to-end framework minimizing decision-making regret from observational data.
result Method outperforms existing baselines across different embedding models.
Hopfield networks improve reaction template prediction for few/zero-shot scenarios.
problem Predicting reaction templates for new molecules in CASP.
method Adapted Hopfield networks to associate reaction templates, molecules, and structural information.
result Significantly improved performance for templates with few or zero training examples.
AM-PPI uses multiple predictors to reduce label cost in healthcare AI.
problem Reduces label cost in post-deployment monitoring of healthcare AI.
method Combines model predictions with a small labeled sample, routing each instance to a cost-appropriate subset of predictors.
result Produces narrower confidence intervals than single-predictor methods.
A new capsule network framework that preserves input transformations.
problem Inefficiency in learning part-whole relationships and lack of equivariance guarantees in capsule networks.
method Proposes a new capsule network framework that learns to projectively encode pose-variations for every capsule-type of each layer using a trainable, equivariant function over a grid of group-transformations.
result The proposed framework is equivariant and preserves the compositional representation of an input under transformations.
BIN-CT optimizes waste collection routes to reduce costs and emissions.
problem Optimal waste collection in urban areas to manage increasing waste volumes.
method Predictive algorithms based on historical and future data for route planning.
result Reduction in distance traveled and operational costs by 70%.
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.
Bayesian algorithm discovers synthetic routes from target molecules.
problem Identifying synthetic routes from desired products.
method Bayesian inference and combinatorial optimization.
result Algorithm rediscovered 80.3% and 50.0% of known synthetic routes.
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.
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.
Paper proposes TRA to learn multiple stock trading patterns.
problem Inconsistent i.i.d. assumption limits stock prediction performance.
method TRA architecture with Optimal Transport for pattern assignment.
result Improves information coefficient (IC) by 0.04-0.06 compared to baselines.
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.
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.
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.
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…
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.
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.
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.
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.
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.
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
problem How disruptions to shipping traffic at chokepoints affect global economy
method Empirically calibrated full-scale agent-based model of global commercial shipping fleet
result Rerouting changes arrival losses under chokepoint closures