Search Results for author: Sirisha Rambhatla

Found 17 papers, 4 papers with code

Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation

no code implementations9 Jun 2022 JinMan Park, Kimathi Kaai, Saad Hossain, Norikatsu Sumi, Sirisha Rambhatla, Paul Fieguth

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.

3D Human Pose Estimation 3D Pose Estimation

Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

1 code implementation9 Jun 2021 Chuizheng Meng, Sirisha Rambhatla, Yan Liu

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.

Federated Learning Spatio-Temporal Forecasting

Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data

1 code implementation14 Dec 2020 Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi, Yan Liu

As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.

PolSIRD: Modeling Epidemic Spread under Intervention Policies

no code implementations3 Sep 2020 Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu

Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies.

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning

1 code implementation NeurIPS 2020 Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

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.

Dictionary Learning

Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes

no code implementations28 Jun 2020 Loc Trinh, Michael Tsang, Sirisha Rambhatla, Yan Liu

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.

DeepFake Detection Face Swapping

Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning

no code implementations15 Jun 2020 Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu

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.

Meta-Learning

COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations

3 code implementations26 Mar 2020 Karishma Sharma, Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu

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.

Fact Checking Misinformation

Provable Online Dictionary Learning and Sparse Coding

no code implementations ICLR 2019 Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

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.

Dictionary Learning

NOODL: Provable Online Dictionary Learning and Sparse Coding

no code implementations28 Feb 2019 Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

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.

Dictionary Learning

TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions

no code implementations26 Feb 2019 Sirisha Rambhatla, Nikos D. Sidiropoulos, Jarvis Haupt

We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data.

Tensor Decomposition

Target-based Hyperspectral Demixing via Generalized Robust PCA

no code implementations26 Feb 2019 Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

In this work, we present a technique to localize targets of interest based on their spectral signatures.

A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing

no code implementations26 Feb 2019 Sirisha Rambhatla, Xingguo Li, Jineng Ren, Jarvis Haupt

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).

A Dictionary Based Generalization of Robust PCA

no code implementations21 Feb 2019 Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

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

no code implementations21 Feb 2019 Sirisha Rambhatla, Xingguo Li, Jineng Ren, Jarvis Haupt

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.

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