1 code implementation • EMNLP 2020 • Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.
Ranked #1 on Stock Market Prediction on stocknet (using extra training data)
1 code implementation • 24 May 2023 • Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang, Jimeng Sun, Jiawei Han
The mission of open knowledge graph (KG) completion is to draw new findings from known facts.
1 code implementation • EMNLP 2021 • Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
1 code implementation • NAACL 2022 • Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso, Vikram Nanda, Sudheer Chava
Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media.
1 code implementation • 30 Mar 2020 • Shivam Agarwal, Siddarth Venkatraman
We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss.
Multimedia Sound Audio and Speech Processing
1 code implementation • ACL 2022 • Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava
Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities.
no code implementations • 28 Jan 2021 • Prashanthi Ramachandran, Shivam Agarwal, Arup Mondal, Aastha Shah, Debayan Gupta
In recent times, ReLU has been found to converge much faster and be more computationally efficient as compared to non-linear functions like sigmoid or tanh.
no code implementations • 30 Mar 2021 • Florian Laurent, Manuel Schneider, Christian Scheller, Jeremy Watson, Jiaoyang Li, Zhe Chen, Yi Zheng, Shao-Hung Chan, Konstantin Makhnev, Oleg Svidchenko, Vladimir Egorov, Dmitry Ivanov, Aleksei Shpilman, Evgenija Spirovska, Oliver Tanevski, Aleksandar Nikov, Ramon Grunder, David Galevski, Jakov Mitrovski, Guillaume Sartoretti, Zhiyao Luo, Mehul Damani, Nilabha Bhattacharya, Shivam Agarwal, Adrian Egli, Erik Nygren, Sharada Mohanty
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
no code implementations • NAACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.
no code implementations • EACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.
no code implementations • COLING 2020 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.
1 code implementation • 7 Mar 2024 • SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).