{"arxiv_id":"2310.12986","incoming":{"papers":1,"sentences":1,"groups":[{"kind":"background","label":"Cites it as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2406.03396","title":"Noisy Data Visualization using Functional Data Analysis","url":"/paper/2406.03396","section":"Introduction","sentence":"Manifold learning has been useful in many fields such as image classification and object detection [ref] , image synthesis and enhancement [ref] , video analysis [ref] , 3D data processing [ref] , analyzing single-cell RNA-sequencing data [ref] , and more [ref]","intent_p":0.86,"contests_p":null}]}],"unlabelled_sentences":0},"outgoing":{"papers":0,"sentences":0,"groups":[],"unlabelled_sentences":0},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2310.12986"}}