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Found 23 papers, 4 papers with code
News Source Credibility Assessment: A Reddit Case Study
In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation.
Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Learning a graph from data is the key to taking advantage of graph signal processing tools.
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion
In this paper, we investigate the problem of recovering the frequency components of a mixture of $K$ complex sinusoids from a random subset of $N$ equally-spaced time-domain samples.
Video Super-Resolution Using a Grouped Residual in Residual Network
Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement.
Quantification of Distributionally Robust Risk of Cascade of Failures in Platoon of Vehicles
In this paper, we propose a distributionally robust risk framework to investigate cascading failures in platoons.
YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
Learning Nonlinear Couplings in Network of Agents from a Single Sample Trajectory
We consider a class of stochastic dynamical networks whose governing dynamics can be modeled using a coupling function.
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications.
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
Bayesian community detection for networks with covariates
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms.
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion
In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA).
Label consistency in overfitted generalized $k$-means
We provide conditions under which the estimated labels are close to a refinement of the true cluster labels.
T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
We evaluate the performance of our method on the YCB-Video dataset.
Real-time Pose Estimation from Images for Multiple Humanoid Robots
Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos.
Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise
However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.
Collaborative Filtering with Smooth Reconstruction of the Preference Function
The performance of the method is evaluated on two benchmark datasets (ML-100k and ML-1M).
Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicating robots.
The Potts-Ising model for discrete multivariate data
We introduce a variation on the Potts model that allows for general categorical marginals and Ising-type multivariate dependence.
Multi Variable-layer Neural Networks for Decoding Linear Codes
In this paper, we introduce a new network architecture by increasing the number of variable-node layers, while keeping the check-node layers unchanged.
Compressibility Measures for Affinely Singular Random Vectors
When considering discrete-domain moving-average processes with non-Gaussian excitation noise, the above results allow us to evaluate the block-average RID and DRB, as well as to determine a relationship between these parameters and other existing compressibility measures.
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
We prove that $\Omega(s\log p)$ samples suffice to learn a sparse Gaussian directed acyclic graph (DAG) from data, where $s$ is the maximum Markov blanket size.
Variable Importance Using Decision Trees
Decision trees and random forests are well established models that not only offer good predictive performance, but also provide rich feature importance information.
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations.
