Papers with AML
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (P18-4)
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| Challenge: | Money laundering (AML) is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets. |
| Approach: | They propose a framework that uses deep learning to augment AML monitoring and investigation . money laundering is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets . |
| Outcome: | The proposed framework reduces time and cost by 30% compared to existing methods . money laundering is the world's third largest "industry" |
LLM Explainability via Attributive Masking Learning (2024.findings-emnlp)
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| Challenge: | In this paper, we introduce Attributive Masking Learning (AML), a method designed for explaining language model predictions by learning input masks. |
| Approach: | They introduce a method for explaining language model predictions by learning input masks and ensuring a significant change in the model's explanation when applying the inverse mask to the input. |
| Outcome: | The proposed method outperforms state-of-the-art explanation methods on multiple benchmarks. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |