Search Results for author: Vijaya Krishna Yalavarthi

Found 6 papers, 4 papers with code

Probabilistic Forecasting of Irregular Time Series via Conditional Flows

no code implementations9 Feb 2024 Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme

Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.

Astronomy Irregular Time Series +1

Forecasting Early with Meta Learning

1 code implementation19 Jul 2023 Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

In the early observation period of a time series, there might be only a few historic observations available to learn a model.

Meta-Learning Multi-Task Learning +1

Forecasting Irregularly Sampled Time Series using Graphs

1 code implementation22 May 2023 Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme

Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences.

Astronomy Multivariate Time Series Forecasting +1

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series

1 code implementation5 Oct 2022 Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme

Results indicate an improvement in negative loglikelihood error by up to 32% on real-world datasets and 85% on synthetic datasets when using the Tripletformer compared to the next best model.

Astronomy Medical Diagnosis +2

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series

1 code implementation24 Aug 2022 Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme

Because of the asynchronous nature, they pose a significant challenge to deep learning architectures, which presume that the time series presented to them are regularly sampled, fully observed, and aligned with respect to time.

Astronomy Classification +3

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach

no code implementations2 Feb 2018 Vijaya Krishna Yalavarthi, Arijit Khan

Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change.

Social and Information Networks 68-06

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