Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models</a> <font color="#6f6f6f">WSJ</font>
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Common research advice #2: say precisely what you want to say
Written as part of the Inkhaven Residency program . As previously mentioned, research feedback I give to more junior research collaborators tends to fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. I talked about doing quick sanity checks in a previous piece . Here, I talk about the second piece of advice: saying precisely what you want to say. Saying precisely what you want to say The second most common feedback is that you should write down precisely what you want to express. One of the most co
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arXiv:2512.11556v4 Announce Type: replace Abstract: Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recent work with MIMO mmWave radar has demonstrated its ability to penetrate cardboard packaging for occluded object classification. However, existing models leave room for improvement and extensions across different sensing frequencies. Building on recent work with MIMO radar for occluded object classification, we propose ACCOR, an attention-enhanced complex-valued contrastive learning approach for radar, enabling robust occluded object classification. ACCOR processes complex-valued IQ radar signals via a complex-valued CNN backbone, a multi-head attention layer and
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