STWalk learns node trajectories in temporal graphs.
problem Analyzing temporal behavior of nodes in time-varying graphs.
method Combines space-walk and time-walk to capture spatio-temporal behavior.
result Effective node trajectory representations learned for change point detection.
MoNODEs improve neural ODEs by separating dynamic states from static factors.
problem Learning non-linear dynamics with variations across trajectories.
method Introduces time-invariant modulator variables to separate dynamic states from static factors.
result Consistently improves model generalization and far-horizon forecasting.
LRF framework predicts and interprets longitudinal response trajectories.
problem Sparse and irregular data in longitudinal studies.
method Longitudinal Random Forest (LRF) framework with adaptive node-wise trajectory estimation.
result LRF outperforms competing methods in predicting and interpreting longitudinal trajectories.
Develops MENT for interpreting and detecting changes in network trajectories.
problem Distortion of network geometry and invalidation of temporal comparisons in dynamic network analysis.
method Develops Multiscale Euclidean Network Trajectories (MENT) framework based on second-moment geometry.
result Validates and interprets network trajectories through isotropic normalization and orthogonal transformations.
A new model captures diffusion dynamics in networks using hidden states.
problem Capturing temporal relationships and hidden content trajectories in network diffusion.
method A topological recurrent neural model that embeds diffusion history as hidden states.
result Good experimental performances for diffusion modeling and prediction.
WiFi helps align and calibrate foot-mounted IMU trajectories.
problem Inertial drift and unknown initial states in FMIP.
method Graph-based SLAM with RSS measurements for WiFi APs.
result Aligns and calibrates trajectories accurately.
New method reconstructs network topology from node-dynamics data.
problem Reconstructing network topology from time-resolved observations of node-dynamics.
method Feature ranking using Random forest and RReliefF to rank node importance.
result Method is robust to various system parameters and depends on dynamical regime.
ACA method improves gradient estimation for neural ODEs, reducing error and training time.
problem Inaccurate gradient estimation methods hinder the performance of neural ODEs on benchmark tasks.
method Adaptive Checkpoint Adjoint (ACA) method that applies trajectory checkpointing, deletes redundant components, and supports adaptive solvers.
result ACA reduces error rate by half and training time by half compared to adjoint and naive methods on image classification tasks.
New measure EC assesses node contributions in nonlinear, time-varying systems.
problem Existing node contribution measures assume linear, time-invariant dynamics, failing for complex, real-world systems.
method Defined 'emergent contribution (EC)' as a dynamical leverage measure from Jacobians of differentiable models.
result EC diverges from average controllability under persistent regime switching and sign reversal, identifying limits of local linearization.
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.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
problem Predicting future motion of vehicles in traffic scenes.
method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.
result Jointly predicting trajectories and interaction modes leads to lower trajectory error.
Framework for continuous-time network data representation learning.
problem Learning reliable representations of dynamic network interactions.
method Three-stage process: intensity estimation, projection learning, evolving node representation construction.
result Trajectories satisfy structural and temporal coherence, providing robust inference.
GRADE models evolving graph dynamics by learning node and community representations.
problem Lack of tools to study temporal community dynamics in evolving graphs.
method GRADE is a probabilistic model that learns evolving node and community representations via a random walk prior and variational inference.
result GRADE outperforms baselines in dynamic link prediction and dynamic community detection.
Paper proposes a new descriptor for early trajectory characterization in matrix iterations.
problem Comparing early behavior of high-dimensional trajectories in nonlinear matrix iterations.
method Develops a two-channel fuzzy coordinate system using F-transform for compact representation.
result The descriptor achieves high R^2 values (mean = 0.6480) in approximating convergence lengths.
Enhanced travel time prediction using deep neural networks and road network information.
problem Improving travel time estimation using deep learning models.
method Proposes incorporating road network information into deep learning models for travel time prediction.
result Improved travel time prediction, especially with limited training data.
A new method for semi-supervised classification using graph walks and reinforcement learning.
problem Efficiently classifying nodes in attributed networks with limited labeled data.
method Proposes a reinforcement learning approach to find optimal paths in the graph for classification.
result The method outperforms existing approaches on multiple datasets.
The paper proposes a method to infer differentiation trees from RNA velocity data.
problem Reconstructing dynamic cellular processes from sequencing data.
method Defining varifold distances between RNA velocity curves to approximate shortest-path distances in a tree.
result The varifold distance method approximates the shortest-path distance in a tree isomorphic to the target differentiation tree.
Traffic actors' future motion predicted using a hybrid graph model.
problem Predicting long-term behaviors of traffic actors in complex scenes.
method A hybrid graph model with nodes for actors and traffic elements, and edges for interaction types.
result TrafficGraphNet achieves state-of-the-art trajectory prediction accuracy.
Model learns spatiotemporal patterns on graphs from longitudinal data.
problem Learning spatiotemporal patterns on graphs from longitudinal data.
method Mixed-effects model with stochastic Expectation-Maximization algorithm (MCMC-SAEM).
result Personalized model accurately predicts cortical thickness maps in patients.
Enhances inference of spreading processes using neural-network priors.
problem Estimating initial states of graph processes from partial observations.
method Bayesian framework with single-layer perceptron neural network for initial states; hybrid BP-AMP algorithm.
result Model exhibits first-order phase transitions, creating a statistical-to-computational gap.
RECON reconstructs regulatory networks from time-course data, reducing spurious edges and preserving true regulatory edges.
problem Reconstructing regulatory networks from time-course data with minimal spurious edges and preserving true regulatory relationships.
method RECON uses an integral-based additive nonparametric ODE model with five methodological advances to reconstruct regulatory networks.
result RECON consistently outperforms existing methods, reducing spurious edges and preserving true regulatory edges across various scenarios.
Deep network solves maze path planning without training.
problem Efficient path planning in large mazes with obstacles.
method Max pooling layers without training.
result Solves mazes with over half a billion nodes in short time.
Enhanced ensemble clustering via fast propagation of cluster-wise similarities.
problem Challenges in exploring higher-level granularity and multi-scale indirect relationships in ensemble clustering.
method A novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks.
result Proposes a new cluster-wise similarity matrix and consensus functions to achieve enhanced co-association and consensus clustering.
Bayesian method infers network topology and dynamics from noisy, sparse measurements.
problem Learning network topology and dynamics from partial, noisy data.
method Developed method uses dynamical structure functions derived from linear stochastic differential equations.
result Method outperforms state-of-the-art methods in various network types.
SDSM extracts statistically significant sub-trajectories from large trajectory datasets.
problem Discerning moving patterns that are more characteristic of one group of trajectories than another.
method Statistically Discriminative Sub-trajectory Mining (SDSM) method using tree representation and permutation-based statistical inference.
result SDSM efficiently extracts statistically significant sub-trajectories from massive trajectory datasets.
Enhances Random Forest for imbalanced functional data classification.
problem Challenges in classifying imbalanced functional data.
method Functional Random Forest with Adaptive Cost-Sensitive Splitting (FRF-ACS).
result Significantly improves minority class recall and predictive performance.
Two algorithms learn Gaussian graphical models from Glauber dynamics trajectories, achieving optimal performance.
problem Learning Gaussian graphical models from a single trajectory of a dependent stochastic process.
method Two algorithms based on dueling-neighborhood search and local statistics built from the update sequence of Glauber dynamics.
result Achieve κ−2 dependence of the information-theoretic lower bounds, mixing-free and signal-optimal. New method for robust trajectory classification without parameters.
problem Trajectory classification accuracy and robustness.
method Parameter-free approach to find best trajectory partition and dimension combination.
result Promising preliminary results show improved robustness.
Optimal sample complexity for learning DDAGs from noisy data.
problem Learning interactions in linear dynamical systems over DAGs.
method Proposed a metric and algorithm based on PSD matrix for reconstruction.
result Optimal sample complexity n=Θ(qlog(p/q)) for learning DDAGs. Develops a new integration theory for financial markets.
problem No classical measure theory applies to financial markets.
method Introduces conditional non-lattice integrals for non-lattice vector spaces.
result Validates the new integration theory through hedging and pricing.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
Paper infers human mobility from sparse trajectories.
problem Modeling and inferring human mobility from sparse trajectory data.
method Proposes a single trajectory inference algorithm and a deep learning architecture for multiple trajectories.
result Deep learning model achieves 2x overall accuracy improvement on sparse trajectories.
Pattern ensembling fills in missing or inaccurate trajectory data.
problem Incompleteness, missing information, and inaccuracies in geolocation data.
method Probabilistically ensemble similar trajectory patterns from the vicinity.
result Reconstructs missing or unreliable trajectory segments effectively.
A new trajectory representation method for AI problems.
problem Trajectory prediction and optimization in AI problems.
method Sub-goal trees, recursively partitioning trajectories into sub-segments.
result Sub-goal trees predict trajectories faster and more accurately.
Study shows magnetic trajectories in Berger spheres are homogeneous.
problem Homogeneity of contact magnetic trajectories in Berger spheres.
method Proved every contact magnetic trajectory is a product of a homogeneous geodesic and a charged Reeb flow.
result Contact magnetic trajectories in Berger spheres are homogeneous.
Improved trajectory prediction for team sports using sparse outputs.
problem Challenging to train deep learning models for player trajectory prediction.
method Sparse trajectory prediction and constant acceleration interpolation.
result Interpolation improves performance for all tested models.
WS-II algorithm segments trajectories with high accuracy.
problem Trajectory segmentation for diverse data sources.
method Supervised learning with sliding window analysis.
result WS-II outperforms other methods in all datasets.
A framework clusters vehicle motion trajectories efficiently.
problem Costly manual annotation of vehicle motion data.
method Five-stage framework: align, embed, extract, embed, cluster.
result Framework achieves promising results on real-world dataset.
TREP learns pedestrian trajectories efficiently without needing full datasets.
problem Learning fixed-length vector representations of variable-length trajectories.
method Actor-critic sequence-to-sequence autoencoder with spatial-aware objective function.
result TREP efficiently learns trajectory representations without needing full datasets.
Proposes a CNN-based method for better trajectory owner prediction.
problem Improves trajectory owner prediction for better personalized recommendations and urban planning.
method Connects POIs in a graph, encodes POIs into vectors, transforms trajectories into matrices, and uses a CNN to detect features and predict owners.
result Significantly outperforms existing methods in various metrics.
New adversarial method detects abnormal trajectories from unlabeled data.
problem Detecting abnormal events from trajectory data without manual threshold setting.
method Generative adversarial network (GAN) framework for unsupervised learning.
result Best accuracy for abnormal trajectory detection on urban traffic videos.
Generative model learns vehicle trajectory distributions for better data generalization.
problem Data sparsity and privacy issues in urban vehicle trajectory analysis.
method Generative adversarial imitation learning framework for urban vehicle trajectory generation.
result TrajGAIL model produces synthetic trajectories similar to real ones, achieving significant performance gains.
CoverNet predicts urban driving trajectories using diverse sets of possible actions.
problem Multimodal probabilistic trajectory prediction for urban driving.
method Frame trajectory prediction as classification over a diverse set of trajectories; dynamically generate sets based on current state.
result Outperforms state-of-the-art methods on real-world self-driving datasets.
Method detects trajectory outliers using Hodge Laplacian embeddings.
problem Detecting outliers in trajectory data on simplicial complexes.
method Flow-embeddings using Hodge 1-Laplacian of simplicial complexes.
result Classifies trajectories based on topological behavior.
Simple model predicts trajectory probabilities.
problem Forecasting trajectories from visual data.
method Spatio-temporal convolutional neural network.
result Achieves results on par with or better than existing methods.
New online algorithm segments and summarizes trajectories efficiently.
problem Efficiently segmenting and summarizing complex trajectory data.
method Point density and natural structure of trajectories.
result Online algorithm improves trajectory visualization and query efficiency.
Study on billiard trajectories with fixed bounces.
problem Counting periodic trajectories with specific bounces.
method Analyzes two-dimensional dispersive billiard systems.
result Asymptotic growth of primitive periodic trajectories.
Regularizes trajectory optimization with denoising autoencoders.
problem Trajectory optimization models are prone to inaccuracies.
method Uses a denoising autoencoder trained on the same trajectories.
result Improves planning with gradient-based and gradient-free optimizers.