| 1 |
$t_0$: A Time-Series Foundation Model for Forecasting with Context |
提出$t_0$模型以解决多变量时间序列预测问题 |
MAE foundation model |
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| 2 |
Lifted Bellman Linear Programming for Offline Reinforcement Learning |
提出Lifted Bellman线性规划以解决离线强化学习中的价值估计问题 |
reinforcement learning offline reinforcement learning |
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| 3 |
Comparing Latent Concept Formation in State Space Models and Transformers via Sparse Autoencoders |
通过稀疏自编码器比较状态空间模型与变换器的潜在概念形成 |
Mamba SSM state space model |
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| 4 |
Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse |
提出药物动力学状态空间模型以预测血流动力学崩溃 |
predictive model Mamba state space model |
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| 5 |
Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision |
提出基于强化学习的黑箱对抗攻击以解决计算机视觉模型脆弱性问题 |
reinforcement learning |
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| 6 |
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation |
通过符号蒸馏学习预后变量以改进气候模型参数化 |
distillation |
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| 7 |
Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap |
提出强化学习以增强运筹学在动态决策中的应用 |
reinforcement learning |
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| 8 |
iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs |
提出iSDFT以解决持续学习中的教师信息控制问题 |
distillation |
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| 9 |
TTSE: A Two-Track Online Self-Evolution Framework |
提出TTSE框架以解决LLM自主进化问题 |
reinforcement learning large language model |
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| 10 |
H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache |
提出H-Spec以解决KV缓存开销问题 |
Mamba large language model |
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| 11 |
Acceptance-Aware Draft Model Training for Speculative Decoding |
提出接受长度感知训练损失以优化推测解码 |
reinforcement learning large language model |
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| 12 |
CLOOPD: Closing the Learner Loop in On-Policy Distillation |
提出CLOOPD框架以优化在线策略蒸馏过程 |
distillation |
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| 13 |
Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective |
提出分布式强化学习以解决部分可观测环境中的不确定性问题 |
reinforcement learning |
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| 14 |
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting |
提出MGRD以解决神经突起形态预测中的不确定性问题 |
MAE spatiotemporal |
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