| 10 |
Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms |
提出CAPE模型和IDB策略,提升心电图对比学习预训练模型泛化性与公平性。 |
contrastive learning foundation model |
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| 11 |
Inpainting-Guided Policy Optimization for Diffusion Large Language Models |
提出IGPO:利用Inpainting引导扩散LLM的强化学习,提升数学问题求解能力 |
reinforcement learning large language model |
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| 12 |
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data |
提出CDQAC算法,通过离线强化学习从随机数据中学习高效作业调度策略 |
reinforcement learning offline RL offline reinforcement learning |
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| 13 |
BenchECG and xECG: a benchmark and baseline for ECG foundation models |
BenchECG:心电图(ECG)基础模型标准化评测与xECG基线模型 |
representation learning foundation model |
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| 14 |
CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction |
CrunchLLM:用于结构化商业推理和结果预测的多任务LLM |
predictive model large language model foundation model |
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| 15 |
Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems |
提出CRL-Pythia,一种面向多核系统的协同强化学习预取架构,提升IPC。 |
reinforcement learning |
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| 16 |
Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks |
提出Fed-MARL框架,解决6G边缘网络中隐私保护和节能的资源管理问题 |
reinforcement learning |
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| 17 |
Prototypical Contrastive Learning For Improved Few-Shot Audio Classification |
提出原型对比学习框架,提升少样本音频分类性能 |
contrastive learning |
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| 18 |
Why and How Auxiliary Tasks Improve JEPA Representations |
提出辅助任务提升JEPA表征质量的理论分析与实践方法 |
latent dynamics model-based RL representation learning |
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