arXiv research

Study learns dynamics of linear systems from multiple short trajectories.

Method predicts NBA players' multi-modal movement trajectories.

This work tackles uncertainty in multi-agent multi-modal trajectory forecasting.

We learn linear models from nonlinear systems using multiple trajectories and regularization.

SafeMIL learns safer policies by avoiding risky behavior from non-preferred trajectories.

Algorithm learns multiple LDS models from unlabeled short trajectories.

MSBM extends SB for multi-marginal trajectory inference.

Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.

Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss…

For a non-vanishing gradient-like vector field on a compact manifold $X^{n+1}$ with boundary, a discrete set of trajectories may be tangent to the boundary with reduced multiplicity $n$, which is the maximum possible. (Among them are trajectories that are tangent to $\partial X$ exactly $n$ times.) We prove a lower bou…

This paper presents several strategies that can improve neural network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM networks enhanced with two forms of regul…

New method learns cell trajectories from multiple snapshots.

Framework learns continuous dynamics from sparse trajectories.

Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel from the raw trajectory data? How can we infer such a mobility model from the single trajectory information? How can we further generalize the mobility inference to ac…

Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, thus, has received a lot of attention in the recent years. While previous works have addressed modeling interactions with the static (obstacl…

EviTrack improves sequential prediction in delayed disambiguation scenarios.

We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame. The Lagrangian trajectories of particles, captured using a high-speed camera and subsequently reconstructed in 3-dimensional space, were used to train a variational autoencoder (VAE) which comprises multipl…

Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.

PlanGAN uses GANs to plan efficient trajectories for multi-goal tasks in sparse reward environments.

Proposes handling ambiguity in sequential data predictions.

In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such sy…

Extends RL to random stopping times, improving optimization.

Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others. Towards these goals, we introduc…

Efficiently trains forward processes to minimize generative trajectories curvature.

MPE models traffic trajectory data to predict next locations.

New method for predicting paths of unpredictable objects with high confidence.

Paper presents a probabilistic framework for diffusion synchronization.

Coordination recognition and subtle pattern prediction of future trajectories play a significant role when modeling interactive behaviors of multiple agents. Due to the essential property of uncertainty in the future evolution, deterministic predictors are not sufficiently safe and robust. In order to tackle the task o…

PhysVarMix predicts diverse urban trajectories with physics constraints.

Study uses vehicle trajectory data to predict traffic incidents on highways.

Patients initially diagnosed with early mild cognitive impairment (eMCI) are known to be a clinically heterogeneous group with very subtle patterns of brain atrophy. To examine the boarders between normal controls (NC) and eMCI, Magnetic Resonance Imaging (MRI) was extensively used as a non-invasive imaging modality to…

Proposes SSC for estimating counterfactual survival trajectories from observational data.

Study of supervised learning from multiple non-independent sequences.

Systems of interacting particles or agents have wide applications in many disciplines such as Physics, Chemistry, Biology and Economics. These systems are governed by interaction laws, which are often unknown: estimating them from observation data is a fundamental task that can provide meaningful insights and accurate …

Estimates network structure and interaction rules from multiple agent trajectories.

Deep learning approximates SPDE solutions from noise trajectories.

Generative models need per-sample confidence scores to improve quality and stability.

Bayesian inference models failure distributions in autonomous systems.

We propose a method to learn a distribution of shape trajectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this tr…

CoTj improves diffusion model quality and stability via graph planning.

Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multi-modal set of possible outcomes in real-world domains such as autonomous driving. Beyond single MAP trajectory prediction, obtaining an accurate probability distri…

New method improves learning from multiple correlated data trajectories.

Unified framework for multi-view diffusion geometries using intertwined diffusion trajectories.

ProtoryNet interprets text sequences using prototype trajectories for better understanding.

Study infers interaction kernels from multiple particle trajectories.

Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives. This short-paper proposes PODNet, Plannable Option Discovery Network, addressing how to segment an unstructured set of demonstrated traject…

We consider the problem of finding optimal strategies that maximize the average growth-rate of multiplicative stochastic processes. For a geometric Brownian motion the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction o…

In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China. We also provide preliminary results of localization and trajec…