{"id":"W3033437302","doi":"10.1007/978-3-030-58526-6_37","title":"Info3D: Representation Learning on 3D Objects Using Mutual Information Maximization and Contrastive Learning","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Infomax; Mutual information; Computer science; Artificial intelligence; Maximization; Feature learning; Representation (politics); Cluster analysis; Unsupervised learning; Pattern recognition (psychology); Machine learning; Mathematics; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002291295,0.001755513,0.002487587,0.001591427,0.0006354694,0.002319969,0.004033449,0.002906655,0.007393979],"category_scores_gemma":[0.005457064,0.001489259,0.002251049,0.001823238,0.001426548,0.004427298,0.004599371,0.003140127,0.002791246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042739,"about_ca_system_score_gemma":0.001350245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003738852,"about_ca_topic_score_gemma":0.004765621,"domain_scores_codex":[0.9987605,0.0003796257,0.00005436186,0.0002800032,0.0004421428,0.00008329989],"domain_scores_gemma":[0.9984634,0.0007778227,0.0000747408,0.0003948456,0.0002105747,0.00007860755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005912841,0.0002621574,0.0004937749,0.0004061173,0.0002958594,0.0001590737,0.0001294019,0.2652803,0.009571429,0.03386179,0.02552908,0.6634196],"study_design_scores_gemma":[0.00003514161,0.00003947491,0.00007414165,0.00001179917,0.00001359321,0.00003695806,0.000006525862,0.9782706,0.003711673,0.01581871,0.001967786,0.00001354396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002419412,0.0001895251,0.9929345,0.0001088522,0.00004020879,0.00004240404,0.0002068825,0.003536942,0.0005212763],"genre_scores_gemma":[0.06448612,0.0002523633,0.9286798,0.0002264352,0.00009460953,0.000262971,0.001471544,0.001993976,0.002532114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007393979,"threshold_uncertainty_score":0.02473533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723232900074114,"score_gpt":0.2470134514731537,"score_spread":0.2297811224724126,"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."}}