查看更多>>摘要：Reporters obtained the following quote from the background information supplied by the inventors:“The present invention relates to package handling systems. Mo re particularly, the invention relates torobotic package handling systems for s orting packages of varying shapes and sizes according to routinginformation on the package.“Delivery of parcels or packages from their origin to their final destination ma y require package routingtransfer stations, and particularly so for packages tr avelling long distances. With billions of packages beingtransported around the globe each day the business of package transport is a significant industry. Suchdeliveries typically involve multiple transportation methodologies including ai r, truck, train, etc., with thepackages having a destination address printed ei ther directly thereon or on a label affixed to the package.
查看更多>>摘要：The following quote was obtained by the news editors from the background informa tion supplied by theinventors: “Machine learning models, such as neural network s, may be used in critical applications suchas in the healthcare, manufacturing , transportation, financial, information technology industries, amongothers. In these and other applications, explanations to a user related to why the model g enerated aspecific prediction for a particular input, what data, models, and pr ocessing have been applied to generatethat prediction, and/or the like can be u seful, and in some instances, required. However, conventionalexplainable machin e learning methods inefficiently and/or inaccurately demonstrate the properties of thehidden units of the models as they fail to address the multi-modal nature of unconstrained latent featureactivation. As a result, explainability methods on conventional models provide inconsistent and unreliableexplanation associat ed with the model output.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Machine learning (ML) is changing the world of computational protein design, wi th data-drivenmethods surpassing biophysical-based methods in experimental succ ess rates.
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