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-{"doi": "0", "chunk-id": "0", "chunk": "Applicability. This chapter provides guidance regarding the applicability of part 107 to civil small unmanned aircraft operations conducted within the NAS. However, part 107 does not apply to the following: Limited recreational operations of UAS that occur in accordance with Title 49 of the United States Code (49 U.S.C.) § 448091; Operations conducted outside the United States; Amateur rockets; Moored balloons; Unmanned free balloons; Kites; Public aircraft operations; and Air carrier operations.", "id": "0", "title": "AC107-2A", "summary": "This AC provides guidance in the areas of airman (remote pilot) certification, aircraft registration and marking, aircraft airworthiness, and the operation of small Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) to promote compliance with the requirements of Title 14 of the Code of Federal Regulations (14 CFR) part 107.", "source": "null, "authors": null, "categories": null, "comment": null, "journal_ref": null, "primary_category": "cs.AI", "published": null, "updated": null, "references": []}
+{"doi": "1102.0183", "chunk-id": "0", "chunk": "High-Performance Neural Networks\nfor Visual Object Classi\fcation\nDan C. Cire\u0018 san, Ueli Meier, Jonathan Masci,\nLuca M. Gambardella and J\u007f urgen Schmidhuber\nTechnical Report No. IDSIA-01-11\nJanuary 2011\nIDSIA / USI-SUPSI\nDalle Molle Institute for Arti\fcial Intelligence\nGalleria 2, 6928 Manno, Switzerland\nIDSIA is a joint institute of both University of Lugano (USI) and University of Applied Sciences of Southern Switzerland (SUPSI),\nand was founded in 1988 by the Dalle Molle Foundation which promoted quality of life.\nThis work was partially supported by the Swiss Commission for Technology and Innovation (CTI), Project n. 9688.1 IFF:\nIntelligent Fill in Form.arXiv:1102.0183v1  [cs.AI]  1 Feb 2011\nTechnical Report No. IDSIA-01-11 1\nHigh-Performance Neural Networks\nfor Visual Object Classi\fcation\nDan C. Cire\u0018 san, Ueli Meier, Jonathan Masci,\nLuca M. Gambardella and J\u007f urgen Schmidhuber\nJanuary 2011\nAbstract\nWe present a fast, fully parameterizable GPU implementation of Convolutional Neural\nNetwork variants. Our feature extractors are neither carefully designed nor pre-wired, but", "id": "1102.0183", "title": "High-Performance Neural Networks for Visual Object Classification", "summary": "We present a fast, fully parameterizable GPU implementation of Convolutional\nNeural Network variants. Our feature extractors are neither carefully designed\nnor pre-wired, but rather learned in a supervised way. Our deep hierarchical\narchitectures achieve the best published results on benchmarks for object\nclassification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with\nerror rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple\nback-propagation perform better than more shallow ones. Learning is\nsurprisingly rapid. NORB is completely trained within five epochs. Test error\nrates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs,\nrespectively.", "source": "http://arxiv.org/pdf/1102.0183", "authors": ["Dan C. Cire\u015fan", "Ueli Meier", "Jonathan Masci", "Luca M. Gambardella", "J\u00fcrgen Schmidhuber"], "categories": ["cs.AI", "cs.NE"], "comment": "12 pages, 2 figures, 5 tables", "journal_ref": null, "primary_category": "cs.AI", "published": "20110201", "updated": "20110201", "references": []}