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ORCIDVijay Ekambaram
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2020 – today
- 2024
[c8]Santosh Palaskar, Vijay Ekambaram, Arindam Jati, Neelamadhav Gantayat, Avirup Saha, Seema Nagar, Nam H. Nguyen, Pankaj Dayama, Renuka Sindhgatta, Prateeti Mohapatra, Harshit Kumar, Jayant Kalagnanam, Nandyala Hemachandra, Narayan Rangaraj:
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data. AAAI 2024: 22962-22968- [i6]Vijay Ekambaram, Arindam Jati, Nam H. Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M. Gifford, Jayant Kalagnanam:
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. CoRR abs/2401.03955 (2024) - [i5]Chandramouli Kamanchi, Sumanta Mukherjee, Kameshwaran Sampath, Pankaj Dayama, Arindam Jati, Vijay Ekambaram, Dzung Phan:
Activations Through Extensions: A Framework To Boost Performance Of Neural Networks. CoRR abs/2408.03599 (2024) - [i4]Debarpan Bhattacharya, Sumanta Mukherjee, Chandramouli Kamanchi, Vijay Ekambaram, Arindam Jati, Pankaj Dayama:
Towards Unbiased Evaluation of Time-series Anomaly Detector. CoRR abs/2409.13053 (2024) - 2023
- [c7]Vijay Ekambaram
, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam:
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. KDD 2023: 459-469 - [c6]Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami:
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. KDD 2023: 891-900 - [i3]Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam:
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. CoRR abs/2306.09364 (2023) - [i2]Santosh Palaskar, Vijay Ekambaram, Arindam Jati, Neelamadhav Gantayat, Avirup Saha, Seema Nagar, Nam H. Nguyen, Pankaj Dayama, Renuka Sindhgatta, Prateeti Mohapatra, Harshit Kumar, Jayant Kalagnanam, Nandyala Hemachandra, Narayan Rangaraj:
AutoMixer for Improved Multivariate Time-Series Forecasting on BizITOps Data. CoRR abs/2310.20280 (2023) - 2022
- [c5]Dhaval Salwala, Seshu Tirupathi, Brian Quanz, Wesley M. Gifford, Stuart Siegel, Vijay Ekambaram, Arindam Jati:
Distributed Incremental Machine Learning for Big Time Series Data. IEEE Big Data 2022: 2356-2363 - [i1]Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami:
Hierarchy-guided Model Selection for Time Series Forecasting. CoRR abs/2211.15092 (2022) - 2020
- [c4]Vijay Ekambaram, Kushagra Manglik, Sumanta Mukherjee, Surya Shravan Kumar Sajja, Satyam Dwivedi, Vikas Raykar:
Attention based Multi-Modal New Product Sales Time-series Forecasting. KDD 2020: 3110-3118
2010 – 2019
- 2017
- [c3]Vijay Ekambaram, Ruhi Sharma Mittal, Prasenjit Dey, Ravindranath Kokku, Aditya K. Sinha, Satya V. Nitta:
Tell Me More: Digital Eyes to the Physical World for Early Childhood Learning. EDM 2017 - 2013
- [c2]Vijay Ekambaram, Krishna M. Sivalingam:
Interest flooding reduction in Content Centric Networks. HPSR 2013: 205-210 - [c1]Vikrant Nandakumar, Vijay Ekambaram, Vivek Sharma:
Appstrument - A Unified App Instrumentation and Automated Playback Framework for Testing Mobile Applications. MobiQuitous 2013: 474-486
Coauthor Index
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