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	<title>carrier-frequency offset &#8211; BIOENGINEER.ORG</title>
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		<title>Neural networks learn hidden parameters from recovered signals, not labels</title>
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		<pubDate>Sat, 17 Oct 2026 17:57:00 +0000</pubDate>
				<category><![CDATA[Chemistry]]></category>
		<category><![CDATA[carrier-frequency offset]]></category>
		<category><![CDATA[channel state information]]></category>
		<category><![CDATA[coil sensitivity maps]]></category>
		<category><![CDATA[differentiable physics]]></category>
		<category><![CDATA[Inverse Problems]]></category>
		<category><![CDATA[label-free learning]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[parallel MRI]]></category>
		<category><![CDATA[PEIL]]></category>
		<category><![CDATA[physics-embedded inverse learning]]></category>
		<category><![CDATA[signal recovery]]></category>
		<category><![CDATA[wireless communications]]></category>
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					<description><![CDATA[A new method called physics-embedded inverse learning trains neural networks to estimate unobservable physical parameters by supervising them with final signal-recovery error, achieving strong results in wireless communications and MRI without parameter labels.]]></description>
		
		
		
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