| 1 |
A Critical Evaluation of AI Feedback for Aligning Large Language Models |
评估AI反馈在大型语言模型对齐中的有效性 |
reinforcement learning large language model instruction following |
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| 2 |
Revisiting Data Augmentation in Deep Reinforcement Learning |
提出数据增强方法以提升深度强化学习的样本效率与泛化能力 |
reinforcement learning deep reinforcement learning DRL |
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| 3 |
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM |
提出MINGLE框架以解决EHR多模态数据融合问题 |
representation learning multimodal |
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| 4 |
In value-based deep reinforcement learning, a pruned network is a good network |
提出逐步剪枝技术以提升价值基深度强化学习网络性能 |
reinforcement learning deep reinforcement learning |
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| 5 |
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning |
提出Reach-Aware Value Learning以解决离边界状态问题 |
reinforcement learning offline reinforcement learning |
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| 6 |
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation |
提出PaSeR以解决低资源环境下图像分割问题 |
reinforcement learning |
✅ |
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| 7 |
Offline Multi-task Transfer RL with Representational Penalization |
提出一种新算法以解决离线多任务强化学习中的表示转移问题 |
reinforcement learning offline RL offline reinforcement learning |
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| 8 |
On the Byzantine-Resilience of Distillation-Based Federated Learning |
提出新防御机制以增强蒸馏基础联邦学习的拜占庭鲁棒性 |
distillation |
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| 9 |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs |
提出DB-LLM以解决超低位量化带来的准确性下降问题 |
distillation large language model |
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| 10 |
Induced Model Matching: Restricted Models Help Train Full-Featured Models |
提出诱导模型匹配方法以提升全特征模型训练效果 |
predictive model distillation |
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