arXiv research

Graph neural network predicts vehicle interactions and trajectories for autonomous driving.

Trajectory-level supervision allows efficient offline reinforcement learning.

A framework clusters vehicle motion trajectories efficiently.

Algorithm learns mixtures of Markov chains and MDPs from short trajectories.

Study shows reinforcement learning is possible with once-per-episode feedback.

Physics-informed diffusion model detects anomalous trajectories in GPS data.

DETECT clusters mobility behaviors from trajectories using deep learning.

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to utilize the exi…

Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features . In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challengin…

New method learns particle system potentials from unlabeled data.

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

Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data …

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…

Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism…

We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furtherm…

KOMET identifies Koopman operators from model parameter trajectories to adapt to evolving data distributions.

Algorithm learns multiple LDS models from unlabeled short trajectories.

Tracking large numbers of densely-arranged, interacting objects is challenging due to occlusions and the resulting complexity of possible trajectory combinations, as well as the sparsity of relevant, labeled datasets. Here we describe a novel technique of collective tracking in the model environment of a 2D honeybee hi…

Label noise in SGD helps converge to flatter minima.

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…

CT-OT Flow estimates continuous-time dynamics from discrete snapshots.

Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate b…

In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the associati…

Forest Fire Clustering discovers cell types from single-cell data.

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…

New method observes learning paths to improve model supervision.

Bayesian methods improve tracking multiple objects through dynamic dependencies.

Early stopping in meta-learning improved by analyzing neural activation patterns.

Proposes handling ambiguity in sequential data predictions.

In recent years, multi object tracking (MOT) problem has drawn attention to it and has been studied in various research areas. However, some of the challenging problems including time dependent cardinality, unordered measurement set, and object labeling remain unclear. In this paper, we propose robust nonparametric met…

Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a learned model of the environment for motion prediction. This modularity explicitly allows for unsupervised adaptation of trajectory prediction models to unseen…

SARA uses similarity to learn rewards robustly and adaptively.

SUPE combines unlabeled data with RL to efficiently explore tasks.

Interactive learning with hindsight instruction feedback achieves better performance than traditional methods.

Identifies latent actions and dynamics from offline data with diverse demonstrators.

Nonparametric Bayesian approaches to clustering, information retrieval, language modeling and object recognition have recently shown great promise as a new paradigm for unsupervised data analysis. Most contributions have focused on the Dirichlet process mixture models or extensions thereof for which efficient Gibbs sam…

RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.

RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.

In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of…

StrADiff separates sources from mixtures without labels, using structured priors.

Deep networks learn clean structure before memorizing corrupted labels, leaving a spectral signature in gradient centered scatter.

Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has motivated the fast development of semi-supervised techniques, whose performance is on a par with or better than supervised approaches. A curr…

We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistica…

Paper uses deep imitation learning to predict aircraft trajectories accurately.

Paper infers human mobility from sparse trajectories.

Pattern ensembling fills in missing or inaccurate trajectory data.

New algorithm trains ReLU gates provably in linear time.

Study shows magnetic trajectories in Berger spheres are homogeneous.