Papers with optimization landscape
Why is unsupervised alignment of English embeddings from different algorithms so hard? (D18-1)
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| Challenge: | a new paper challenges word embedding algorithms to align independent English word embeds with 100% precision . authors show that when two different embeddables are used, they fail to do so . |
| Approach: | They propose to use unsupervised bilingual dictionary induction to study English-English alignments. |
| Outcome: | The proposed approach is more of a challenge than a technical contribution . it shows that the results challenge unsupervised bilingual dictionary induction algorithms . |
Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers (2025.emnlp-main)
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| Challenge: | Existing approaches to optimize attention for long sequences have been limited by their computational cost. |
| Approach: | They propose a framework that infuses partial differential equations into the Transformer’s attention mechanism to better handle long sequences. |
| Outcome: | The proposed framework achieves consistent performance gains over standard and long-sequence Transformer variants across a range of tasks. |