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<a href="/2023/01/05/%E6%88%92%E7%83%9F%E6%89%8B%E8%AE%B0/" class="post-title-link" itemprop="url">戒烟手记</a>
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<p>简单分享一下自己戒烟的点点滴滴和主观感受</p>
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<p>介绍是年来自AAAI的北京大学团队的论文《TS2Vec： Towards Universal Representation of Time Series》，介绍了一种更具泛化性的时序数据的表征模型，利用了多层对比学习的方法，使得其能胜任更多时序任务。</p>
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<a href="/2022/11/27/Improved%20Denoising%20Diffusion%20Probabilistic%20Models/" class="post-title-link" itemprop="url">Improved DDPM</a>
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<p>本文是来自OpenAI团队关于Diffusion模型的经典工作《Improved Denoising Diffusion Probabilistic Models》，通过调整噪声规划方式、引入重要性采样、对采样方差进行预测等多种方法提高了diffusion模型的采样质量与速度。</p>
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<a href="/2022/11/22/CSDI%20Conditional%20Score-based%20Diffusion%20Models%20for%20Probabilistic%20Time%20Series%20Imputation/" class="post-title-link" itemprop="url">CSDI：适用于时间序列补全、预测任务的条件分数扩散模型</a>
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<p>介绍来自斯坦福大学Ermon团队论文《CSDI Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation》，了解如何将扩散模型应用在时间序列数据的任务当中。</p>
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<a href="/2022/11/22/Diffusion-based%20Time%20Series%20Imputation%20and%20Forecasting%20with%20Structured%20State%20Space%20Models/" class="post-title-link" itemprop="url">利用基于扩散模型的结构化状态空间模型，适配时间序列的补全、预测任务</a>
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<p>介绍论文《Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models》，了解如何将结构化状态空间模型应用在时间序列数据的任务当中。</p>
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<p>介绍来自斯坦福大学团队的文章《Diffusion-LM Improves Controllable Text Generation》，概览怎样用扩散概率模型处理离散的序列数据，同时这个过程中还利用到了clamp技巧，提高了训练效果。</p>
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<p>【未完待续】从万有引力的角度，理解扩散生成模型中的常微分方程建模的意义，并对Poisson流生成模型进行解析，期冀可以为扩散模型提供一个更为坚实的理论基础，同时更便于理解。</p>
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<p>介绍论文Understanding Diffusion Models A Unified Perspective，文章将详细介绍diffusion模型的数学建模细节及其来由，同时揭示了噪声预测和分数预测之前的关联。</p>
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<p>介绍来自斯坦福大学Ermon团队宋博士的论文《Score-Based Generative Modeling through Stochastic Differential Equations》，结合苏剑林老师关于随机微分方程的解析博客，尝试揭示连续时间尺度中扩散模型的运行机理。</p>
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<p>介绍来自人大团队的文章《Multiscale complexity fluctuation behaviours of stochastic interacting cryptocurrency price model》，文章利用了一种多智能体模型结合随机过程的方法，对市场中的羊群效应进行模拟，同时采用了多尺度模糊熵、匹配能算法衡量了市场数据的复杂程度。</p>
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