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.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,236 papers · 148 categories
Trend · papers per month
48 results for “graph-based SLAM”
SLAM-net learns to navigate visually in challenging indoor environments.
problem Challenges in SLAM for visual robot navigation, especially in noisy conditions.
method Differentiable SLAM Network (SLAM-net) that encodes a particle filter SLAM algorithm in a differentiable graph and learns components through backpropagation.
result Significantly outperforms ORB-SLAM in noisy conditions and improves the Habitat Challenge 2020 PointNav task.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
A scalable 3D magnetic field SLAM method using smartphone data.
problem Scalable 3D magnetic field SLAM in buildings and objects.
method Gaussian process model, reduced-rank regression, hexagonal tiling, Rao-Blackwellised particle filter.
result Accurate position and orientation estimates from smartphone data.
Deep factorization machines improved SLAM task performance.
problem Improving second language acquisition modeling.
method Used deep factorization machines to model pairwise relationships.
result Achieved AUC 0.815, beating logistic regression baseline.
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functionscs.LG
New algorithm speeds up online mapping of unknown terrains.
problem Increasing computational demands of GP mapping as area expands.
method Recursive GP mapping using local basis functions in an information filter.
result Reduces overall computational complexity and speeds up mapping.
We use copulas to improve SLAM in uncertain environments.
problem Uncertain data association and nonlinear transition models in SLAM.
method Integrate copulas into a Sequential Monte Carlo estimator for SLAM.
result Our method effectively handles SLAM in uncertain environments.
We present a novel spectral learning algorithm for simultaneous localization and mapping (SLAM) from range data with known correspondences. This algorithm is an instance of a general spectral system identification framework, from which it inherits several desirable properties, including statistical consistency and no l…
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
problem Lack of uncertainty estimates and real-time capability in SLAM.
method Combines differentiable rendering and 6-DoF dynamics, uses approximations for Bayesian inference.
result Runs at 10Hz real-time with similar accuracy to state-of-the-art SLAM.
New algorithm optimizes pose graphs for SFM and SLAM.
problem Optimizing pose graphs for structure from motion and simultaneous localization and mapping.
method Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm.
result Robust initialization and uncertainty estimates for reliable solutions.
Proposes Gaussian Processes for more accurate time-correlated measurement noise in robotics.
problem Time-correlated measurement noise in robotics applications.
method Gaussian Processes as a non-parametric model for correlated measurement noise.
result Improved performance of Kalman filtering with Gaussian Processes.
EchoFusion tracks and reconstructs fetal images without external trackers.
problem Limited capture range and view-dependent artefacts in fetal ultrasound imaging.
method Combining deep learning and SLAM for image-based tracking and volume reconstruction.
result Demonstrated robust tracking and accurate volume reconstruction in fetal ultrasound.
New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspectivemath.ST
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Bayesian nonparametric approach using unlabeled data for graph-based learning.
result Posterior contracts optimally around the truth with sufficient unlabeled data.
Graph-based MAB system improves recommendation accuracy.
problem Improving recommendation accuracy in user space.
method Proposes a graph-based recommendation system that learns user space geometry.
result Simulation results show improvements over state-of-the-art MAB algorithms.
End-to-end graph-based SSL learns all graph factors dynamically.
problem Learning quality of graph in SSL is crucial but difficult.
method Proposes an end-to-end approach to optimize all graph factors.
result Demonstrates effectiveness on benchmark datasets.
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
Paper develops a method to identify graphs and filters from filtered signals.
problem Learning graphs and filters from filtered signals.
method Developed an algorithm to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations.
result The proposed algorithm outperforms current state-of-the-art methods.
Improves graph-based active learning for non-Gaussian models.
problem Efficiently selecting data points for labeling in graph-based semi-supervised learning.
method Approximates non-Gaussian distributions, introduces rank-one update and model change acquisition function.
result Enhanced active learning for graph-based SSL under non-Gaussian models.
Galerkin method outperforms graph-based methods in spectral decompositions.
problem Improving spectral decomposition methods in machine learning.
method Restricting study to a small set of test functions using the Galerkin method.
result Statistical and computational superiority of Galerkin method over graph-based approaches.
Paper presents a graph-based semi-supervised method for hyperspectral image classification.
problem Hyperspectral image classification with limited labeled data.
method Novel superpixel algorithm based on spectral covariance matrix, followed by superpixel graph construction and classification.
result The method outperforms state-of-the-art approaches, especially in scenarios with minimal labeled data.
Paper proves supermodularity of AG-SSL objective and proposes a greedy sampling algorithm.
problem Improving semi-supervised learning with limited labeled data.
method Proves supermodularity of AG-SSL objective under Stieltjes regularization and proposes a greedy sampling algorithm.
result Proposed method achieves superior classification accuracy compared to state-of-the-art methods.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Poisoning attacks improve graph-based recommender system recommendations.
problem Designing effective poisoning attacks for graph-based recommender systems.
method Formulated as an optimization problem, solved with techniques to assign rating scores to fake users.
result Outperforms existing attacks for graph-based recommender systems, recommending target items to 580 times more normal users.
Graph-based weather prediction adapted for local models.
problem Applying neural weather prediction to limited area modeling.
method Adapting graph-based Neural Weather Prediction approach to local models.
result Validation of multi-scale hierarchical model extension for Nordic region.
The paper proves the consistency of graph-based semi-supervised learning.
problem Proving the consistency of graph-based semi-supervised learning.
method Non-parametric framework, enforcing estimated scores to observed responses for labeled data, tuning parameter for unenforced scores.
result Consistency of graph-based learning is proved under certain conditions.
On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithmsstat.ML
Graph-based Bayesian learning theory ensures scalable algorithms for large datasets.
problem Consistency and scalability in semi-supervised learning with graphs.
method Introduces new scaling theory for graph parameters and proves uniform spectral gaps for Markov chain Monte Carlo algorithms.
result Graph-based Markov chain Monte Carlo algorithms have a uniform spectral gap independent of unlabeled data size.
The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.
problem Graph-based semi-supervised learning in high-dimensional data.
method Laplace Learning in the Wasserstein space, proving variational convergence and characterizing the Laplace-Beltrami operator.
result Consistent classification performance in high-dimensional settings.
Flexible framework for semi-supervised learning on graphs.
problem Predicting unlabeled graph data using limited labeled data.
method Generative framework leveraging features, graph structure, and labels.
result Outperforms state-of-the-art models in most settings.
Novel graph-based approach segments financial integration eras.
problem Assessing worldwide financial integration using data patterns.
method 3-step approach combining graph-based representations and optimization.
result Endogenous stable eras of world-wide financial integration found.
A graph-based evolutionary algorithm automates machine learning workflows.
problem Automated machine learning to reduce manual operations.
method Graph-based architecture for flexible model combinations, evolutionary algorithm with mutation and heredity operators, Bayesian hyper-parameter optimization.
result State-of-the-art performance compared to other AutoML systems.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
This paper detects function-level obfuscation in binary code using graph-based methods.
problem Detecting and characterizing function-level obfuscation in binary code.
method Graph-based approaches, including GNNs, are compared on various datasets.
result GNNs outperform baselines in function-level obfuscation detection, especially in a 11-class classification task.
GATES improves neural architecture search by modeling operations as information transformation.
problem Improving predictor-based neural architecture search efficiency.
method GATES models operations as information transformation, covering both node and edge cell search spaces.
result GATES boosts sample efficiency and improves predictor performance.
Graph-based method predicts edge flows from partial measurements.
problem Predicting edge flows from limited measurements.
method Graph-based semi-supervised learning with flow conservation constraints.
result Strong performance on synthetic and real-world flow networks.
Novel graph-based framework for hyperspectral image classification using superpixels.
problem High classification accuracy with limited labelled data in hyperspectral images.
method Superpixel method for defining local regions, spectral and spatial features extraction, contracted graph representation, semi-supervised classifier.
result Our approach produces accurate classifications with minimal labelled data, outperforming state-of-the-art techniques.
Improved graph-based semi-supervised learning with model change active learning.
problem Identifying which unlabelled data points to label to best improve classifier performance.
method Pairing model change active learning with graph-based semi-supervised learning methods.
result Improved multiclass classification performance over prior methods.
The paper analyzes consistency of graph-based semi-supervised learning methods for binary and multi-class classification.
problem Consistency of semi-supervised learning algorithms on graphs with noisy labels and well-clustered unlabelled data.
method The study examines graph-based probit and one-hot encoding methods for binary and multi-class classification, analyzing the consistency of optimization-based techniques.
result The analysis reveals insights into the rational function choice for optimization, improving the consistency of semi-supervised learning algorithms.
Proposes a new algorithm for graph-based semi-parametric contextual bandits.
problem Non-stationarity in human behavior and social interaction.
method SemiGraphTS algorithm for graph-based semi-parametric reward model.
result Derives an upper bound of cumulative regret for graph-based semi-parametric model.
Poisson learning improves graph-based semi-supervised learning at very low label rates.
problem Degeneracy of Laplacian semi-supervised learning at low label rates.
method Replaces label assignment with source and sink placement, solving Poisson equation.
result Provably more stable and informative predictions than Laplacian learning.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
problem Inconsistency in multi-view data affects clustering performance.
method Integrates consistent and inconsistent parts of multiple views using a unified matrix.
result Demonstrates improved clustering performance on real-world datasets.
Novel online graph-based method detects changes in high-dimensional data.
problem Challenges in detecting changes in high-dimensional data.
method Graph-based similarity measure derived from graph-spanning ratio.
result High detection power and controlled false alarm rate for high-dimensional data.
Proposes a method to learn a low-rank kernel matrix for graph-based clustering.
problem Challenges in learning an optimal kernel matrix for graph-based clustering.
method Unified framework for graph construction and kernel learning, focusing on a low-rank kernel matrix.
result Efficacy of the proposed method validated through extensive experiments.
New graph-based method selects outlier ensemble components.
problem Poor components negatively affect consensus results in outlier ensembles.
method Mapping rankings to graphs, mining to identify subsets.
result Our method outperforms state-of-the-art techniques.
Graph-based rehearsal improves continual learning performance.
problem Catastrophic forgetting in continual learning models.
method Augmenting a memory array with a learnable random graph to capture pairwise similarities.
result Our model consistently outperforms baselines on task-free continual learning benchmarks.
Paper introduces graph-based transforms for video compression.
problem Efficiently represent video signals for compression.
method Develops two techniques for designing graph-based transforms (GL-GBTs and EA-GBTs).
result Graph-based transforms outperform traditional KLT in video compression.
Paper proposes graph-based separable transforms for video coding.
problem Improving video coding efficiency by better capturing residual block statistics.
method Derives graph-based separable transforms (GBSTs) from line graphs with weights determined by parameters.
result GBSTs achieve about 0.4% average coding gain over existing transforms in VVC.
Bayesian models for uncertainty in graph-based data classification.
problem Uncertainty quantification in high-dimensional data classification.
method Bayesian models based on graph semi-supervised learning.
result Unified framework for various classification methods.