{"id":"W1902934009","doi":"10.48550/arxiv.1511.00363","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","year":2015,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1835,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Canadian Institute for Advanced Research; Polytechnique Montréal","funders":"","keywords":"MNIST database; Computer science; Dropout (neural networks); Artificial neural network; Deep neural networks; Binary number; Invariant (physics); Computation; Range (aeronautics); Artificial intelligence; Permutation (music); Computer engineering; Simple (philosophy); Deep learning; Algorithm; Machine learning; Arithmetic; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877200430149616,"score_gpt":0.2391499425125286,"score_spread":0.2203779382110324,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}