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Jelena Kovacevic
New York University
231Publications
39H-index
9,210Citations
Publications 212
Newest
#1Jingxiao Liu (CMU: Carnegie Mellon University)
#2Siheng Chen (CMU: Carnegie Mellon University)H-Index: 15
Last.Mario Bergés (CMU: Carnegie Mellon University)H-Index: 3
view all 9 authors...
We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more...
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Rohan Varma (CMU: Carnegie Mellon University)H-Index: 6
#2Harlin Lee (CMU: Carnegie Mellon University)H-Index: 1
Last.Jelena Kovacevic (NYU: New York University)H-Index: 39
view all 4 authors...
In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of nonconvex regularizers that exhibit superior recovery performance over existing convex ones. We present theoretical results in the form of asymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposed optimization problem and analyz...
1 CitationsSource
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Rohan Varma (CMU: Carnegie Mellon University)H-Index: 6
#2Jelena Kovacevic (NYU: New York University)H-Index: 39
Product graphs are a useful way to model richer forms of graph-structured data that can be multi-modal in nature. In this work, we study the reconstruction or estimation of smooth signals on product graphs from noisy measurements. We motivate and present representations and algorithms that exploit the inherent structure in product graphs for better and more computationally efficient recovery. These contributions stem from the key insight that smooth graph signals on product graphs can be structu...
1 CitationsSource
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Chaojing Duan (CMU: Carnegie Mellon University)H-Index: 1
#2Siheng Chen (Mitsubishi Electric Research Laboratories)H-Index: 15
Last.Jelena Kovacevic (NYU: New York University)H-Index: 39
view all 3 authors...
We present a neural-network-based architecture for 3D point cloud denoising called neural projection denoising (NPD). In our previous work, we proposed a two-stage denoising algorithm, which first estimates reference planes and follows by projecting noisy points to estimated reference planes. Since the estimated reference planes are inevitably noisy, multi-projection is applied to stabilize the denoising performance. NPD algorithm uses a neural network to estimate reference planes for points in ...
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#1Rohan VarmaH-Index: 6
#2Harlin LeeH-Index: 1
Last.Yuejie ChiH-Index: 23
view all 4 authors...
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle inequality, we establish the statistical error rates of first-order stationary points of the prop...
#1Jingxiao Liu (CMU: Carnegie Mellon University)
#2Mario Bergés (CMU: Carnegie Mellon University)H-Index: 3
Last.Hae Young Noh (CMU: Carnegie Mellon University)H-Index: 13
view all 6 authors...
Source
#1Chaojing Duan (CMU: Carnegie Mellon University)H-Index: 1
#2Siheng Chen (Uber )H-Index: 15
Last.Jelena Kovacevic (NYU: New York University)H-Index: 39
view all 3 authors...
We present a novel algorithm for 3D point cloud denoising called weighted multi-projection. As a collection of 3D points sampled from surfaces of objects, a 3D point cloud is widely used in robotics, autonomous driving and augmented reality. Due to the physical limitations of 3D sensing devices, 3D point clouds are usually noisy, which influences subsequent computations. Compared to many previous denoising works, instead of directly smoothing the coordinates of 3D points, we use a two-fold smoot...
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#1Anuva Kulkarni (CMU: Carnegie Mellon University)H-Index: 2
#2Franz Franchetti (CMU: Carnegie Mellon University)H-Index: 27
Last.Jelena Kovacevic (CMU: Carnegie Mellon University)H-Index: 39
view all 3 authors...
We describe and analyze a co-design of algorithm and software for high-performance simulation of a partial differential equation (PDE) numerical solver for large-scale datasets. Large-scale scientific simulations involving parallel Fast Fourier Transforms (FFTs) have extreme memory requirements and high communication cost. This hampers high resolution analysis with fine grids. Moreover, it is difficult to accelerate legacy Fortran scientific codes with modern hardware such as GPUs because of mem...
Source
#1Yaoqing Yang (CMU: Carnegie Mellon University)H-Index: 11
#2Siheng Chen (Uber )H-Index: 15
Last.Jelena Kovacevic (NYU: New York University)H-Index: 39
view all 6 authors...
We consider a problem of localizing a temporal path signal that evolves over time on a graph. A path signal represents the trajectory of a moving agent on a graph in a series of consecutive time stamps. Through combining dynamic programing and graph partitioning, we propose a path-localization algorithm with significantly reduced computational complexity. To analyze the localization performance, we use two evaluation metrics to quantify the localization error: the Hamming distance and the destin...
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In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and recovery of bandlimited graph signals that lie on them. Product graphs are graphs that are composed from smaller graph atoms; we motivate how this model is a flexible and useful way to model richer classes of data that can be multi-modal in nature. Previous works have established a sampling theory on graphs for bandlimited signals. Importantl...
1 Citations
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