pepr-hal-data / softcite /AssessingPerSampleMembershipInferenceVulnerabilitywithoutRetraining.software.json
| {"application": "software-mentions", "version": "0.9.0-SNAPSHOT", "revision": "54efbc6", "date": "2026-10-14T14:52+0000", "md5": "11E7EB656DF672470EF12F9B6534C449", "biblio": {"title": "Assessing Per-Sample Membership Inference Vulnerability without Retraining", "authors": "Valentin Dorseuil, Jamal Atif, Olivier Capp\u00e9"}, "mentions": [{"type": "software", "software-type": "software", "software-name": {"rawForm": "AdamW", "normalizedForm": "AdamW", "offsetStart": 122, "offsetEnd": 127}, "context": "Optimizers, learning rates, batch sizes, weight decay and number of epochs follow standard recipes for each architecture (AdamW for vision and text, SGD-momentum for ResNet-20, Adam for the regression MLPs); the exact hyper-parameters are listed in Appendix C.1 and in the release scripts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "ResNet", "normalizedForm": "ResNet", "offsetStart": 129, "offsetEnd": 135}, "version": {"rawForm": "20", "normalizedForm": "20"}, "context": "We cover both classification and regression on tabular, vision and text data: (i) CIFAR-10 with a small CNN; (ii) CIFAR-100 with ResNet-20; (iii)", "mentionContextAttributes": {"used": {"value": true, "score": 0.9978440999984741}, "created": {"value": false, "score": 5.364418029785156e-06}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999708354473114}, "created": {"value": false, "score": 5.364418029785156e-06}, "shared": {"value": false, "score": 1.0728836059570312e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "SGD-momentum", "normalizedForm": "SGD-momentum", "offsetStart": 149, "offsetEnd": 161}, "context": "Optimizers, learning rates, batch sizes, weight decay and number of epochs follow standard recipes for each architecture (AdamW for vision and text, SGD-momentum for ResNet-20, Adam for the regression MLPs); the exact hyper-parameters are listed in Appendix C.1 and in the release scripts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "ResNet", "normalizedForm": "ResNet", "offsetStart": 159, "offsetEnd": 165}, "version": {"rawForm": "20", "normalizedForm": "20"}, "context": "SST-2 (BERT linear probe) CalHousing (Ridge) CalHousing (MLP) 10 -2 10 -1 10 0 10 1 10 -2 10 -1 10 0 10 1 10 2 CIFAR-10 (CNN) 10 -2 10 -1 10 0 10 1 CIFAR-100 (ResNet-20) 10 -2 10 -1 10 0 10 1 BloodMNIST (DINOv2) 10 -2 10 -1 10 0 10 1 UCI Energy (MLP) top-k percentile (%) recall (%) Loss \u2113 i \u2207\u2113 i \u2206\u2113 NS i \u2206\u2113 IF i random y = x 16-model mean single model \u00b11 std band", "mentionContextAttributes": {"used": {"value": true, "score": 0.999708354473114}, "created": {"value": false, "score": 2.384185791015625e-06}, "shared": {"value": false, "score": 1.0728836059570312e-06}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999708354473114}, "created": {"value": false, "score": 5.364418029785156e-06}, "shared": {"value": false, "score": 1.0728836059570312e-06}}}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "code", "normalizedForm": "code", "offsetStart": 160, "offsetEnd": 164}, "context": "All experiments were run on an Nvidia-V100 GPU cluster; the full pipeline, training, statistics, attacks, scores and figures, is reproducible from the released code through the per-dataset launch scripts, and will be released publicly upon acceptance of the paper.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9997692704200745}, "created": {"value": false, "score": 0.002186298370361328}, "shared": {"value": false, "score": 2.682209014892578e-05}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9997692704200745}, "created": {"value": false, "score": 0.002186298370361328}, "shared": {"value": false, "score": 2.682209014892578e-05}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "ResNet", "normalizedForm": "ResNet", "offsetStart": 166, "offsetEnd": 172}, "version": {"rawForm": "20", "normalizedForm": "20", "offsetStart": 173, "offsetEnd": 175}, "context": "Optimizers, learning rates, batch sizes, weight decay and number of epochs follow standard recipes for each architecture (AdamW for vision and text, SGD-momentum for ResNet-20, Adam for the regression MLPs); the exact hyper-parameters are listed in Appendix C.1 and in the release scripts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999708354473114}, "created": {"value": false, "score": 5.364418029785156e-06}, "shared": {"value": false, "score": 1.0728836059570312e-06}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "Adam", "normalizedForm": "Adam", "offsetStart": 177, "offsetEnd": 181}, "context": "Optimizers, learning rates, batch sizes, weight decay and number of epochs follow standard recipes for each architecture (AdamW for vision and text, SGD-momentum for ResNet-20, Adam for the regression MLPs); the exact hyper-parameters are listed in Appendix C.1 and in the release scripts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "scripts", "normalizedForm": "scripts", "offsetStart": 196, "offsetEnd": 203}, "context": "All experiments were run on an Nvidia-V100 GPU cluster; the full pipeline, training, statistics, attacks, scores and figures, is reproducible from the released code through the per-dataset launch scripts, and will be released publicly upon acceptance of the paper.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9997692704200745}, "created": {"value": false, "score": 0.002186298370361328}, "shared": {"value": false, "score": 2.682209014892578e-05}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9997692704200745}, "created": {"value": false, "score": 0.002186298370361328}, "shared": {"value": false, "score": 2.682209014892578e-05}}}, {"type": "software", "software-type": "implicit", "software-name": {"rawForm": "scripts", "normalizedForm": "scripts", "offsetStart": 281, "offsetEnd": 288}, "context": "Optimizers, learning rates, batch sizes, weight decay and number of epochs follow standard recipes for each architecture (AdamW for vision and text, SGD-momentum for ResNet-20, Adam for the regression MLPs); the exact hyper-parameters are listed in Appendix C.1 and in the release scripts.", "mentionContextAttributes": {"used": {"value": false, "score": 0.4508855938911438}, "created": {"value": false, "score": 3.5762786865234375e-06}, "shared": {"value": false, "score": 3.5762786865234375e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9997692704200745}, "created": {"value": false, "score": 0.002186298370361328}, "shared": {"value": false, "score": 2.682209014892578e-05}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "ResNet", "normalizedForm": "ResNet", "offsetStart": 448, "offsetEnd": 454}, "version": {"rawForm": "20", "normalizedForm": "20"}, "context": "Training Attacks+Stats Surrogates IF (full network) Total Dataset / Model per-model 200 refs 16 targets 200 refs per-target per-target GPU-h UCI Energy / MLP 30 s 1.7 h 8 min 30 s 3 s -1.7 h California / Linear reg. 1 min 3.3 h 0.3 h 7 min 10 s -3.6 h California / MLP 1 min 3.3 h 0.3 h 7 min 10 s 20 min 8.9 h BloodMNIST / DINO (linear) 15 min 60.0 h 4.0 h 5 min 15 s -64.0 h CIFAR-10 / CNN 4 min 13.0 h 1.0 h 20 min 30 s 2.2 h 49.2 h CIFAR-100 / ResNet-20 13 min 44.4 h 3.5 h 45 min 1 min -48.5 h SST-2 / BERT-tiny (full FT) 6 min 20 h 1.6 h 17 min 45 s -20.6 h SST-2 / BERT-base (linear) 19 min 64.6 h 5.0 h 18 min 3 min -70 h Total 266 GPU-h", "mentionContextAttributes": {"used": {"value": true, "score": 0.8995323777198792}, "created": {"value": false, "score": 1.9073486328125e-06}, "shared": {"value": false, "score": 4.76837158203125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.999708354473114}, "created": {"value": false, "score": 5.364418029785156e-06}, "shared": {"value": false, "score": 1.0728836059570312e-06}}}], "references": [], "runtime": 13193, "id": "f973e238d5940aa5dd6cb92e37d090054634c05d", "metadata": {"id": "f973e238d5940aa5dd6cb92e37d090054634c05d"}, "original_file_path": "../../projects/softcite-analysis-hal/data/grobid/xml/AssessingPerSampleMembershipInferenceVulnerabilitywithoutRetraining.grobid.tei.xml", "file_name": "AssessingPerSampleMembershipInferenceVulnerabilitywithoutRetraining.grobid.tei.xml"} |