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Found 21 papers, 6 papers with code
Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning
This work examines a semi-blind single-channel source separation problem.
A Dictionary Based Generalization of Robust PCA
We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing method.
A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging
We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing method.
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing task(s).
TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions
We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data.
Target-based Hyperspectral Demixing via Generalized Robust PCA
In this work, we present a technique to localize targets of interest based on their spectral signatures.
NOODL: Provable Online Dictionary Learning and Sparse Coding
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients.
Provable Online Dictionary Learning and Sparse Coding
To this end, we develop a simple online alternating optimization-based algorithm for dictionary learning, which recovers both the dictionary and coefficients exactly at a geometric rate.
COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Although the knowledge of governing partial differential equations (PDE) of data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems.
How does this interaction affect me? Interpretable attribution for feature interactions
Feature attribution is a way to analyze the impact of features on predictions.
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations.
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
To this end, we develop a provable algorithm for online structured tensor factorization, wherein one of the factors obeys some incoherence conditions, and the others are sparse.
PolSIRD: Modeling Epidemic Spread under Intervention Policies
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies.
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues.
Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation
Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera.
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding
To this end, we develop an Importance Sampling based distance metric -- I-SEA -- which enjoys the properties of a metric while consistently achieving superior performance for machine learning tasks such as classification and representation learning.
Domain Generalization for Domain-Linked Classes
This requires a class to be expressed in multiple domains for the learning algorithm to break the spurious correlations between domain and class.
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?
Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignment (such as generative modeling and stylization) and feature-level alignment (which matches the distribution of the feature maps, e. g. gradient reversal layers).
Domain-Guided Masked Autoencoders for Unique Player Identification
We further introduce a new spatio-temporal network leveraging our novel d-MAE for unique player identification.
