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<a href="/2024/10/16/DL/ActiveLearning/ActiveLearning/" class="post-title-link" itemprop="url">Active Learning Literature Survey</a>
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<p>介绍Active Learning的基本概念与算法,以及相关python库——ALiPy的使用。</p>
<blockquote>
<p>The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns.</p>
</blockquote>
<p>Reference:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://www.semanticscholar.org/paper/Active-Learning-Literature-Survey-Settles/818826f356444f3daa3447755bf63f171f39ec47">Active Learning Literature Survey</a></li>
<li><a target="_blank" rel="noopener" href="https://arxiv.org/pdf/1901.03802">ALiPy: Active Learning in Python</a></li>
<li><a target="_blank" rel="noopener" href="https://github.com/NUAA-AL/alipy">GitHub:ALiPy</a></li>
</ul>
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<a href="/2024/09/18/Phys/TNMC/TNMC/" class="post-title-link" itemprop="url">Tensor network Monte Carlo simulations for the two-dimensional random-bond Ising model</a>
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<time title="Modified: 2024-09-21 23:10:51" itemprop="dateModified" datetime="2024-09-21T23:10:51+08:00">2024-09-21</time>
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<p>Tensor Network Monte Carlo (TNMC) method将张量网络和蒙特卡洛模拟结合,是一种新的模拟方法。本文分为两个部分,介绍TNMC方法,以及其在随机二维Ising模型上的实验。</p>
<p>Link:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://arxiv.org/abs/2409.06538">Tensor network Monte Carlo simulations for the two-dimensional random-bond Ising model</a></li>
<li><a target="_blank" rel="noopener" href="https://arxiv.org/abs/1507.00767">Unbiased Monte Carlo for the age of tensor networks</a></li>
</ul>
<p>Code:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://github.com/Fermichen99/TNMC">TNMC</a></li>
</ul>
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<a href="/2024/08/27/Phys/Annealing_root_finding/Annealing_root_finding/" class="post-title-link" itemprop="url">Annealing approach to root finding</a>
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<p>在数值分析和科学计算中,Newton-Raphson方法是一个非常重要的工具,它被广泛用于求解方程的根。然而,经典的Newton-Raphson方法在面对复杂的非线性方程和多个根的情况下,可能会出现收敛性差、振荡或发散的情况。为了解决这些问题,研究者们提出了一种基于物理学启发的新方法,该方法在保留Newton-Raphson方法优点的同时,通过引入一个新的参数$\beta$,有效提升了算法的收敛速度和稳定性。</p>
<p>Link:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.025305">Annealing approach to root finding</a></li>
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<a href="/2024/08/26/DL/LossPlasticity/LossPlasticity/" class="post-title-link" itemprop="url">Loss of plasticity in deep continual learning</a>
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<time title="Created: 2024-08-26 10:30:00 / Modified: 20:56:10" itemprop="dateCreated datePublished" datetime="2024-08-26T10:30:00+08:00">2024-08-26</time>
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<p>文章主要讨论在持续学习任务中,损失具有弹性是很关键,进而指出在深度学习中,仅仅依靠反向传播是不够的,需要结合随机、非梯度的优化方式(例如演化计算等)。</p>
<p>Link:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://doi.org/10.1038/s41586-024-07711-7">Loss of plasticity in deep continual learning</a></li>
<li><a target="_blank" rel="noopener" href="https://mp.weixin.qq.com/s?__biz=MzkxODQ0MTQzMg==&mid=2247489568&idx=1&sn=606d85bf9c0df3780f4dc3ccd5835c8d&chksm=c0a474736003b8a50446944aee862eb1417da7025d4892f919e940496e644a24d700688cdb9d&mpshare=1&scene=1&srcid=0825em2QJV5witmRbRpL25ZX&sharer_shareinfo=58591a65fb8a82b6749c7ee6829e99a3&sharer_shareinfo_first=58591a65fb8a82b6749c7ee6829e99a3#rd">Nature正刊(演化深度持续学习)Loss of plasticity in deep continual learning</a></li>
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<a href="/2024/08/25/DL/Injectivity_ReLU/InjecReLU/" class="post-title-link" itemprop="url">Injectivity of ReLU networks (perspectives from statistical physics)</a>
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<p>对于ReLU激活层单向性的分析,得到变化的上下界。</p>
<p>Link:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://arxiv.org/abs/2302.14112">Injectivity of ReLU networks: perspectives from statistical physics</a></li>
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<a href="/2024/08/24/RL/SoG/SoG/" class="post-title-link" itemprop="url">Student of Games:Aunified learning algorithm forboth perfect andimperfect information games</a>
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<time title="Created: 2024-08-24 12:00:00" itemprop="dateCreated datePublished" datetime="2024-08-24T12:00:00+08:00">2024-08-24</time>
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<p>在之前的游戏引擎中存在仅适用于单一游戏的问题,虽然AlphaZero解决对于完全完美信息的通用性,但是对于扑克等依旧不存在通用算法。这篇文章提出一种新的通用算法Student of Games(SoG),该算法类似于AlphaZero通过自博弈的方式完成训练,同时拓展了适用边界——适用于非完全信息博弈,例如扑克。</p>
<font color="red">这个算法结合了很多内容,并没有读懂</font>
<p>Link:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://www.science.org/doi/10.1126/sciadv.adg3256#supplementary-materials">Student of Games:Aunified learning algorithm forboth perfect andimperfect information games</a></li>
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<a href="/2024/08/21/ai4sci/MLRG/MLRG/" class="post-title-link" itemprop="url">Machine learning renormalization group for statistical physics</a>
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<time title="Created: 2024-08-21 12:00:00 / Modified: 13:07:11" itemprop="dateCreated datePublished" datetime="2024-08-21T12:00:00+08:00">2024-08-21</time>
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<p>通过构建三个网络学习实空间重整化过程中的参数变化。</p>
<p>Reference:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://doi.org/10.1088/2632-2153/ad0101">Machine learning renormalization group for statistical physics</a></li>
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<a href="/2024/07/09/Phys/science_3831989/RSP/" class="post-title-link" itemprop="url">Analytic and Algorithmic Solution of Random Satisfiability Problems</a>
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<time title="Created: 2024-07-09 10:37:00 / Modified: 12:17:21" itemprop="dateCreated datePublished" datetime="2024-07-09T10:37:00+08:00">2024-07-09</time>
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<p>Parisi关于组合优化问题的分析。</p>
<p>reference:</p>
<ul>
<li><a target="_blank" rel="noopener" href="https://www.jstor.org/stable/3831989">Analytic and Algorithmic Solution of Random Satisfiability Problems</a></li>
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