California Digital Library
PulseAugur coverage of California Digital Library — every cluster mentioning California Digital Library across labs, papers, and developer communities, ranked by signal.
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New Calibratable Disambiguation Loss Improves AI Classifier Reliability
Researchers have introduced a new method called Calibratable Disambiguation Loss (CDL) to improve the reliability of classifiers in Multi-Instance Partial-Label Learning (MIPL) tasks. This plug-and-play loss function en…
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New framework automates analytic geometry problem generation with neural-symbolic approach
Researchers have developed FormalAnalyticGeo, a novel framework designed to automatically generate multimodal analytic geometry problems. This system utilizes a neural-symbolic approach, employing a formal language call…
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New framework automates analytic geometry problem generation with AI · 2 sources tracked
Researchers have developed FormalAnalyticGeo, a novel framework designed to automatically generate multimodal analytic geometry problems. This system utilizes a neural-symbolic approach, employing a formal language call…