Papers with training convergence
Lipschitz Constrained Parameter Initialization for Deep Transformers (2020.acl-main)
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| Challenge: | Existing studies show that deep Transformers have difficulty in training even with residual connection and layer normalization. |
| Approach: | They propose a method that leverages the Lipschitz constraint on the initialization of Transformer parameters to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. |
| Outcome: | The proposed model outperforms previous RNN/CNN models but fails to converge with the original computation order. |
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (P18-1)
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| Challenge: | Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. |
| Approach: | They propose a sentence-level policy gradient method that bridges computation between two neural networks in a hierarchical way while maintaining language fluency. |
| Outcome: | The proposed model achieves state-of-the-art on all metrics and higher abstractiveness scores on the CNN/Daily Mail dataset and faster training convergence than previous models. |
MemeWeaver: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection (2026.findings-eacl)
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| Challenge: | Existing methods to detect hate speech on social media are limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. |
| Approach: | They propose a multimodal framework for detecting sexism and misogyny using a graph reasoning mechanism that can be used to train multiple visual-textual fusion strategies. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on MAMI and EXIST benchmarks while achieving faster training convergence. |
DiVISe: Direct Visual-Input Speech Synthesis Preserving Speaker Characteristics And Intelligibility (2025.findings-naacl)
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| Challenge: | Video-to-speech (V2S) synthesis requires acoustic hints to accurately reconstruct both speech content and speaker characteristics from video clips alone. |
| Approach: | They propose a video-to-speech (V2S) model that predicts Mel-spectrograms directly from video frames. |
| Outcome: | The proposed model outperforms existing models in acoustic intelligibility and preserves speaker-specific characteristics. |
Fair Federated Learning with Biased Vision-Language Models (2024.findings-acl)
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| Challenge: | Existing literature ignores the inherent group unfairness within CLIP and its ethical implications on FL applications. |
| Approach: | They propose a fairness-aware adaptation framework for CLIP in federated learning . they propose to leverage biased pre-trained VLMs to build fair FL frameworks . |
| Outcome: | The proposed framework addresses unique bias in FL, triggered by data heterogeneity . it trains a fair FL model with fairness-aware deep visual prompting (DVP) Extensive results on human face attribute recognition (FAR) applications show it outperforms state-of-the-art FL models . |
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to text generation use discrete text within a continuous diffusion space, which incurs substantial computational overhead during training and results in slower sampling speeds. |
| Approach: | They propose a soft absorbing state that facilitates diffusion models in learning to reconstruct discrete mutations based on the underlying Gaussian space. |
| Outcome: | The proposed method accelerates training convergence by 4x and generates samples of similar quality 800x faster, rendering it closer to practical application. |
UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding (2026.findings-acl)
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| Challenge: | Consistency models (CMs) have shown promise in the efficient generation of both image and text. |
| Approach: | They propose to use a discrete token for both image and text generation to achieve a unified denoising perspective. |
| Outcome: | The proposed model outperforms SD3 on GenEval and Image Reward while being 1.5 faster at long-sequence generating speed. |