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		<title>生成准确率高达 95%，人工智能结合机器人技术增强可穿戴电子材料设计</title>
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		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Sun, 23 Jun 2024 08:59:51 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[技术]]></category>
		<category><![CDATA[数据]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>设计具有定制电气和机械性能的超轻导电气凝胶，对于各种电子设备的应用都至关重要。传统方法依赖于在广阔的参数空间中进行迭代、耗时的实验。</p>
<p>The post <a href="https://www.itsiwei.com/28375.html">生成准确率高达 95%，人工智能结合机器人技术增强可穿戴电子材料设计</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>驱动新型生成式人工智能的物理过程</title>
		<link>https://www.itsiwei.com/28232.html</link>
					<comments>https://www.itsiwei.com/28232.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Thu, 21 Sep 2023 06:46:36 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>一些现代图像生成器依靠扩散原理来创建图像。基于带电粒子分布背后的过程的替代方案可能会产生更好的结果。</p>
<p>The post <a href="https://www.itsiwei.com/28232.html">驱动新型生成式人工智能的物理过程</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>用于化学动力学模拟的原子神经网络表示</title>
		<link>https://www.itsiwei.com/27954.html</link>
					<comments>https://www.itsiwei.com/27954.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Thu, 29 Dec 2022 07:39:31 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[化学]]></category>
		<category><![CDATA[机器学习]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>机器学习技术已广泛应用于化学、物理、生物学和材料科学的许多领域。</p>
<p>The post <a href="https://www.itsiwei.com/27954.html">用于化学动力学模拟的原子神经网络表示</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>分析了自家150个ML模型之后，这家全球最大的旅行网站得出了6条经验教训</title>
		<link>https://www.itsiwei.com/25728.html</link>
					<comments>https://www.itsiwei.com/25728.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Thu, 21 Nov 2019 05:50:03 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[技术]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>本文是对这篇论文的简短总结。</p>
<p>The post <a href="https://www.itsiwei.com/25728.html">分析了自家150个ML模型之后，这家全球最大的旅行网站得出了6条经验教训</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>7个月后，GPT-2的“假新闻威胁论”被证明是虚惊一场</title>
		<link>https://www.itsiwei.com/25273.html</link>
					<comments>https://www.itsiwei.com/25273.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Tue, 10 Sep 2019 12:33:52 +0000</pubDate>
				<category><![CDATA[业界资讯]]></category>
		<category><![CDATA[技术]]></category>
		<category><![CDATA[数据]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>AI 前线导读：今年年初，OpenAI 推出了当时号称“最强 NLP 模型”的 GPT-2，该模型可以生成连贯的文本段落，刷新了 7 大数据集基准，并且能在未经预训练的情况下，完成阅读理解、问答、机器翻译等多项不同的语言建模任务。</p>
<p>The post <a href="https://www.itsiwei.com/25273.html">7个月后，GPT-2的“假新闻威胁论”被证明是虚惊一场</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>如何为模型选择合适的度量标准？</title>
		<link>https://www.itsiwei.com/22425.html</link>
					<comments>https://www.itsiwei.com/22425.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Mon, 14 May 2018 10:42:10 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[技术]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>大多数的博客更多都关注模型的精度、召回率、AUC(Area under curve，ROC曲线下区域面积)等分类指标。这里想稍稍改变一下，让我们来探索各种更多的指标，包括在回归问题中使用的指标。MAE和RMSE是关于连续变量的两个最普遍的度量标准。</p>
<p>The post <a href="https://www.itsiwei.com/22425.html">如何为模型选择合适的度量标准？</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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		<title>论强化学习和概率推断的等价性：一种全新概率模型</title>
		<link>https://www.itsiwei.com/22272.html</link>
					<comments>https://www.itsiwei.com/22272.html#respond</comments>
		
		<dc:creator><![CDATA[IT思维]]></dc:creator>
		<pubDate>Sat, 05 May 2018 03:48:32 +0000</pubDate>
				<category><![CDATA[人工智能]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[强化学习]]></category>
		<category><![CDATA[模型]]></category>
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					<description><![CDATA[<p>虽然强化学习问题的一般形式可以有效地推理不确定性，但强化学习和概率推断的联系并不是很明显。</p>
<p>The post <a href="https://www.itsiwei.com/22272.html">论强化学习和概率推断的等价性：一种全新概率模型</a> first appeared on <a href="https://www.itsiwei.com">IT思维</a>.</p>]]></description>
		
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