| 1 |
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP |
提出可转移表示学习方法以提升CLIP的零-shot迁移能力 |
representation learning zero-shot transfer |
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| 2 |
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? |
揭示开源大语言模型对不当内容生成的脆弱性 |
reinforcement learning RLHF large language model |
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| 3 |
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning |
提出悲观非线性最小二乘值迭代以解决离线强化学习问题 |
reinforcement learning offline RL offline reinforcement learning |
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| 4 |
Solving the Quadratic Assignment Problem using Deep Reinforcement Learning |
提出深度强化学习方法以解决二次分配问题 |
reinforcement learning deep reinforcement learning |
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| 5 |
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity |
提出维度依赖适应性以提升多批次强化学习的样本效率 |
reinforcement learning offline reinforcement learning |
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| 6 |
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression |
提出REMEDI框架以解决原发性硬化性胆管炎的代谢建模问题 |
reinforcement learning |
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| 7 |
An Investigation of Representation and Allocation Harms in Contrastive Learning |
探讨对比学习中的表示与分配损害问题 |
contrastive learning |
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| 8 |
Linear attention is (maybe) all you need (to understand transformer optimization) |
提出线性化Transformer模型以理解优化问题 |
linear attention |
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