Papers with end-to-end differentiable training paradigm
Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training (2024.emnlp-main)
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| Challenge: | Neural networks are increasingly prevalent across a wide range of applications, driving significant advancements in fields such as natural language processing, computer vision, and beyond. |
| Approach: | They propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. |
| Outcome: | The proposed model is capable of classifying a sample and scoring input tokens without any explicit supervision and produces class-wise rationales without instabilities. |