Search Results for author: Shivam Agarwal

Found 12 papers, 7 papers with code

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

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)

Decision Making Stock Market Prediction

HYPHEN: Hyperbolic Hawkes Attention For Text Streams

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.

Stock Price Prediction

HypMix: Hyperbolic Interpolative Data Augmentation

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.

Adversarial Robustness Data Augmentation

Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy

1 code implementation7 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).


Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models

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.

Quantitative Day Trading from Natural Language using Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL) +1

FAST: Financial News and Tweet Based Time Aware Network for Stock Trading

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.


S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

no code implementations28 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.

Privacy Preserving

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

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.

Language Modelling Sentiment Analysis

Deep Residual Neural Networks for Image in Speech Steganography

1 code implementation30 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

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