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	<title>self-knowledge distillation &#8211; BIOENGINEER.ORG</title>
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		<title>Teaching Tiny Networks: New Quantization Method Pushes 1-Bit AI Toward Full-Precision Accuracy</title>
		<link>https://bioengineer.org/teaching-tiny-networks-new-quantization-method-pushes-1-bit-ai-toward-full-precision-accuracy/</link>
		
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		<pubDate>Mon, 05 Oct 2026 12:23:10 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[binary neural networks]]></category>
		<category><![CDATA[CIFAR-10]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[edge AI]]></category>
		<category><![CDATA[knowledge distillation]]></category>
		<category><![CDATA[low-bit inference]]></category>
		<category><![CDATA[model compression]]></category>
		<category><![CDATA[neural network efficiency]]></category>
		<category><![CDATA[quantization-aware training]]></category>
		<category><![CDATA[self-knowledge distillation]]></category>
		<category><![CDATA[structured pruning]]></category>
		<category><![CDATA[TinyML]]></category>
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					<description><![CDATA[Researchers in South Korea have developed a progressive distillation framework called ASBQ that trains one-bit binary neural networks to match full-precision accuracy, reaching 93.1 percent on CIFAR-10.]]></description>
		
		
		
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