Vijaya Krishna Yalavarthi

According to our database1, Vijaya Krishna Yalavarthi authored at least 15 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2024
Probabilistic Forecasting of Irregular Time Series via Conditional Flows.
CoRR, 2024

GraFITi: Graphs for Forecasting Irregularly Sampled Time Series.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Forecasting Irregularly Sampled Time Series using Graphs.
CoRR, 2023

Forecasting Early with Meta Learning.
Proceedings of the International Joint Conference on Neural Networks, 2023

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
Tripletformer for Probabilistic Interpolation of Asynchronous Time Series.
CoRR, 2022

Open Set Recognition for Time Series Classification.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series.
Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

2019
Gait Verification using Deep Learning with a Pairwise Loss.
Proceedings of the 2019 International Conference of the Biometrics Special Interest Group, 2019

2018
A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors.
Proc. VLDB Endow., 2018

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach.
CoRR, 2018

Steering Top-k Influencers in Dynamic Graphs via Local Updates.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017
Probabilistic Entity Resolution with Imperfect Crowd.
CoRR, 2017

Select Your Questions Wisely: For Entity Resolution With Crowd Errors.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016
A Novel Incremental Class Learning Technique for Multi-class Classification.
Proceedings of the Advances in Neural Networks - ISNN 2016, 2016


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