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5 changes: 4 additions & 1 deletion 2024/03/12/cyber_security/encryption-algorithm/index.html
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<a href="/tags/AES/">#AES</a>

<a href="/tags/safety/">#safety</a>

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5 changes: 4 additions & 1 deletion 2024/07/22/cyber_security/asymmetric-encryption/index.html
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<a href="/tags/Diffie-Hellman-encryption/">#Diffie-Hellman encryption</a>

<a href="/tags/safety/">#safety</a>

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4 changes: 2 additions & 2 deletions 2024/10/13/paper/NLP/LLM/RAG-for-NLP-A-Survey/index.html
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<meta name="description" content="《NBV Planning Considering Confidence Obtained From Shape Completion Learning》 From IEEE Robotics and Automation Letters (2024) 一、拟解决问题 对于类似“金属薄板”的目标，难以估计其三维原貌 异常区域：反射光干扰 自遮挡：弯曲板面遮挡自身形状">
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<h1 style="display: none">『paper-CV-1』《NBV-planning》</h1>
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<a href="/tags/next-best-view/">#next best view</a>

<a href="/tags/3D-reconstruction/">#3D reconstruction</a>

<a href="/tags/point-cloud/">#point cloud</a>

<a href="/tags/3D-reconstruction/">#3D reconstruction</a>

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<div>『paper-CV-1』《NBV-planning》</div>
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6 changes: 3 additions & 3 deletions 2024/11/19/paper/NLP/LLM/sneaky-prompt/index.html
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<i class="iconfont icon-tags"></i>

<a href="/tags/safety/">#safety</a>

<a href="/tags/reinforcement-learning/">#reinforcement learning</a>

<a href="/tags/safety/">#safety</a>

<a href="/tags/jailbreak/">#jailbreak</a>

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8 changes: 4 additions & 4 deletions 2024/11/22/paper/MM/agent/navigate-the-risks/index.html
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<a href="/tags/LLM/">#LLM</a>

<a href="/tags/Multimodal/">#Multimodal</a>

<a href="/tags/safety/">#safety</a>

<a href="/tags/multimodal/">#multimodal</a>

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11 changes: 9 additions & 2 deletions 2024/11/29/paper/CV/AML/PGD/index.html
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<meta property="og:description" content="《Towards Deep Learning Models Resistant to Adversarial Attacks》 From International Conference on Learning Representations (2018) 一、本文贡献 将过去的深度学习模型攻击&#x2F;防御方法融合到同一个框架下，抵抗广泛而非单一的攻击类别 证明了 Projecte">
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<li><p>威胁模型：对于每个数据点 <span class="math inline">\(x \in
\mathbb{R}^d\)</span>，设定一个小扰动范围 <span class="math inline">\(\mathcal{S} \subseteq \mathbb{R}^d\)</span>，使得
<span class="math inline">\(F_{\theta}(x + \delta) \ne
F_{\theta}(x)\)</span></p></li>
F_{\theta}(x)\)</span>，<span class="math inline">\(\delta \in
\mathcal{S}\)</span></p></li>
<li><p>鞍点优化问题：相较于标准的神经网络任务，<u>先对输入进行<strong>扰动</strong></u>，再<strong><u>最小化对抗训练损失</u></strong>得到最优模型参数
<span class="math inline">\(\theta^{*}\)</span> <span class="math display">\[
\begin{align}
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