cs.LG(2024-12-06)

📊 共 13 篇论文

🎯 兴趣领域导航

支柱九:具身大模型 (Embodied Foundation Models) (7) 支柱二:RL算法与架构 (RL & Architecture) (4) 支柱六:视频提取与匹配 (Video Extraction) (2)

🔬 支柱九:具身大模型 (Embodied Foundation Models) (7 篇)

#题目一句话要点标签🔗
1 Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization 提出WaveToken,一种基于小波变换的时间序列预测基础模型,实现高效token化。 foundation model
2 The BrowserGym Ecosystem for Web Agent Research BrowserGym生态系统:用于Web Agent研究的统一评估与开发平台 large language model
3 HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Design HiVeGen:一种基于分层LLM的可扩展芯片设计Verilog生成框架 large language model
4 APOLLO: SGD-like Memory, AdamW-level Performance APOLLO:通过近似梯度缩放,在LLM优化中实现AdamW级性能和SGD级内存效率。 large language model
5 Chemist-aligned retrosynthesis by ensembling diverse inductive bias models RetroChimera:通过集成多样归纳偏置模型实现化学家对齐的逆合成 zero-shot transfer
6 Direct Quantized Training of Language Models with Stochastic Rounding 提出基于随机舍入的语言模型直接量化训练方法,降低训练时的内存占用。 large language model
7 IterL2Norm: Fast Iterative L2-Normalization 提出IterL2Norm以解决Transformer模型的层归一化效率问题 large language model

🔬 支柱二:RL算法与架构 (RL & Architecture) (4 篇)

#题目一句话要点标签🔗
8 Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning with Human-AI Feedback 提出基于人类-AI反馈强化学习的LLM物理问题求解增强方法 reinforcement learning PPO DPO
9 Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning 提出NNCL-TLLM,通过近邻对比学习增强LLM在时间序列预测中的性能。 contrastive learning large language model
10 A Temporally Correlated Latent Exploration for Reinforcement Learning 提出时序相关潜在探索(TeCLE),增强强化学习在噪声环境下的探索能力 reinforcement learning deep reinforcement learning
11 Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task 提出迭代训练决策树算法,成功应用于真实机器人倒立摆控制任务。 reinforcement learning deep reinforcement learning DRL

🔬 支柱六:视频提取与匹配 (Video Extraction) (2 篇)

#题目一句话要点标签🔗
12 Multi-Objective Alignment of Large Language Models Through Hypervolume Maximization 提出HaM算法,通过最大化超体积实现大语言模型的多目标对齐 HuMoR large language model
13 Learning Hidden Physics and System Parameters with Deep Operator Networks 提出深度算子网络以解决隐藏物理和系统参数识别问题 sparse sensors

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