{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/sample-gaussian","entry":"sample_gaussian","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":15,"n_papers_ran":4,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":15,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":16,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":11},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2607.05153","paper":"/paper/arxiv-2607-05153","title":"Geometric Causal Models","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"EWeinstein/GCM","path":"spatial_gcm.py","file_url":"https://github.com/EWeinstein/GCM/blob/HEAD/spatial_gcm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"244b89c12e0f9844","mcp_get_code":{"code_sha256":"244b89c12e0f9844"}},{"arxiv_id":"2606.12733","paper":"/paper/arxiv-2606-12733","title":"Let's Ask Gauss: Improved One-Run Privacy Auditing","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"stoneboat/dpsgd-auditbench","path":"src/classifier/white_box_dp_sgd.py","file_url":"https://github.com/stoneboat/dpsgd-auditbench/blob/HEAD/src/classifier/white_box_dp_sgd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b50a90bb6b0af6b9","mcp_get_code":{"code_sha256":"b50a90bb6b0af6b9"}},{"arxiv_id":"2411.17525","paper":"/paper/pushing-the-limits-of-large-language-model","title":"Pushing the Limits of Large Language Model Quantization via the Linearity Theorem","date":"2024-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goodevening13/aquakv","path":"aquakv/grid_generator.py","file_url":"https://github.com/goodevening13/aquakv/blob/HEAD/aquakv/grid_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f5f502f02b4bc42d","mcp_get_code":{"code_sha256":"f5f502f02b4bc42d"}},{"arxiv_id":"2410.00983","paper":"/paper/robust-guided-diffusion-for-offline-black-box","title":"Robust Guided Diffusion for Offline Black-Box Optimization","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggchen1997/rgd","path":"lib/utils.py","file_url":"https://github.com/ggchen1997/rgd/blob/HEAD/lib/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"facb0fb3832a74fc","mcp_get_code":{"code_sha256":"facb0fb3832a74fc"}},{"arxiv_id":"2403.09605","paper":"/paper/counterfactual-contrastive-learning-robust","title":"Counterfactual contrastive learning: robust representations via causal image synthesis","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biomedia-mira/counterfactual-contrastive","path":"causal_models/hvae.py","file_url":"https://github.com/biomedia-mira/counterfactual-contrastive/blob/HEAD/causal_models/hvae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f71d1270abe6581b","mcp_get_code":{"code_sha256":"f71d1270abe6581b"}},{"arxiv_id":"2402.01607","paper":"/paper/natural-counterfactuals-with-necessary","title":"Natural Counterfactuals With Necessary Backtracking","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuangyuanHao/natural_counterfactuals","path":"src-2o/vae.py","file_url":"https://github.com/GuangyuanHao/natural_counterfactuals/blob/HEAD/src-2o/vae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6717f15242b1a22b","mcp_get_code":{"code_sha256":"6717f15242b1a22b"}},{"arxiv_id":"2402.01607","paper":"/paper/natural-counterfactuals-with-necessary","title":"Natural Counterfactuals With Necessary Backtracking","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuangyuanHao/natural_counterfactuals","path":"src-2o/simple_vae.py","file_url":"https://github.com/GuangyuanHao/natural_counterfactuals/blob/HEAD/src-2o/simple_vae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c3673aea1cda139","mcp_get_code":{"code_sha256":"0c3673aea1cda139"}},{"arxiv_id":"2308.16212","paper":"/paper/retrobridge-modeling-retrosynthesis-with","title":"RetroBridge: Modeling Retrosynthesis with Markov Bridges","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"igashov/retrobridge","path":"src/frameworks/diffusion_utils.py","file_url":"https://github.com/igashov/retrobridge/blob/HEAD/src/frameworks/diffusion_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4ce22ba6e93d5f06","mcp_get_code":{"code_sha256":"4ce22ba6e93d5f06"}},{"arxiv_id":"2303.01274","paper":"/paper/measuring-axiomatic-soundness-of","title":"Measuring axiomatic soundness of counterfactual image models","date":"2023-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biomedia-mira/causal-gen","path":"src/vae.py","file_url":"https://github.com/biomedia-mira/causal-gen/blob/HEAD/src/vae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6717f15242b1a22b","mcp_get_code":{"code_sha256":"6717f15242b1a22b"}},{"arxiv_id":"2205.03766","paper":"/paper/scheduled-multi-task-learning-for-neural-chat","title":"Scheduled Multi-task Learning for Neural Chat Translation","date":"2022-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xl2248/sml","path":"thumt-sml/thumt/models/contextual_transformer.py","file_url":"https://github.com/xl2248/sml/blob/HEAD/thumt-sml/thumt/models/contextual_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5dc3b8e8f47d2b8","mcp_get_code":{"code_sha256":"b5dc3b8e8f47d2b8"}},{"arxiv_id":"2112.06351","paper":"/paper/neural-point-process-for-learning","title":"Neural Point Process for Learning Spatiotemporal Event Dynamics","date":"2021-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rose-stl-lab/deepstpp","path":"src/model.py","file_url":"https://github.com/rose-stl-lab/deepstpp/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"41dcba471efe8ce7","mcp_get_code":{"code_sha256":"41dcba471efe8ce7"}},{"arxiv_id":"2008.02792","paper":"/paper/caspr-learning-canonical-spatiotemporal-point","title":"CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations","date":"2020-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davrempe/caspr","path":"caspr/models/utils.py","file_url":"https://github.com/davrempe/caspr/blob/HEAD/caspr/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9503f70580415e65","mcp_get_code":{"code_sha256":"9503f70580415e65"}},{"arxiv_id":"1812.08985","paper":"/paper/non-adversarial-image-synthesis-with","title":"Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors","date":"2018-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yedidh/glann","path":"utils.py","file_url":"https://github.com/yedidh/glann/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"9e9a6dc9e188fa21","mcp_get_code":{"code_sha256":"9e9a6dc9e188fa21"}},{"arxiv_id":"1810.10191","paper":"/paper/making-sense-of-vision-and-touch-self","title":"Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks","date":"2018-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-iprl-lab/multimodal_representation","path":"multimodal/models/models_utils.py","file_url":"https://github.com/stanford-iprl-lab/multimodal_representation/blob/HEAD/multimodal/models/models_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c2dce1e9a43381ec","mcp_get_code":{"code_sha256":"c2dce1e9a43381ec"}},{"arxiv_id":"1802.06552","paper":"/paper/are-generative-classifiers-more-robust-to","title":"Are Generative Classifiers More Robust to Adversarial Attacks?","date":"2018-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepgenerativeclassifier/DeepBayes","path":"models/conv_encoder_cifar10.py","file_url":"https://github.com/deepgenerativeclassifier/DeepBayes/blob/HEAD/models/conv_encoder_cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce4a47b072be0891","mcp_get_code":{"code_sha256":"ce4a47b072be0891"}},{"arxiv_id":"1506.02564","paper":"/paper/gradient-free-hamiltonian-monte-carlo-with","title":"Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families","date":"2015-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karlnapf/kernel_hmc","path":"kernel_hmc/densities/gaussian.py","file_url":"https://github.com/karlnapf/kernel_hmc/blob/HEAD/kernel_hmc/densities/gaussian.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"ff81502c713bcd2f","mcp_get_code":{"code_sha256":"ff81502c713bcd2f"}}]}