Papers by Atula Tejaswi Neerkaje
Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction (2024.lrec-main)
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Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, Dinesh Manocha
| Challenge: | Recent advances in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, but limitations exist. |
| Approach: | They propose a Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. |
| Outcome: | The proposed technique outperforms state-of-the-art methods by 3-7% on financial earnings and conference call datasets. |
RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)
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| Challenge: | Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms. |
| Approach: | They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities. |
| Outcome: | The proposed model abstains from 84% incorrect predictions on Reddit data while out-predicting state of the art models upto 3.5x earlier. |