{"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/logmeanexp","entry":"logmeanexp","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":8,"n_papers_ran":5,"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":6,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"unverified":3},"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":"2409.09787","paper":"/paper/bendem-a-boltzmann-sampler-based-on","title":"BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching","date":"2024-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tonyauyeung/BNEM","path":"dem/models/dds_module.py","file_url":"https://github.com/tonyauyeung/BNEM/blob/HEAD/dem/models/dds_module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cded7f5b04d3ed38","mcp_get_code":{"code_sha256":"cded7f5b04d3ed38"}},{"arxiv_id":"2404.17249","paper":"/paper/making-better-use-of-unlabelled-data-in","title":"Making Better Use of Unlabelled Data in Bayesian Active Learning","date":"2024-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fbickfordsmith/epig","path":"src/math.py","file_url":"https://github.com/fbickfordsmith/epig/blob/HEAD/src/math.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"50077f14e5894773","mcp_get_code":{"code_sha256":"50077f14e5894773"}},{"arxiv_id":"2402.05098","paper":"/paper/on-diffusion-models-for-amortized-inference","title":"Improved off-policy training of diffusion samplers","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GFNOrg/gfn-diffusion","path":"energy_sampling/utils.py","file_url":"https://github.com/GFNOrg/gfn-diffusion/blob/HEAD/energy_sampling/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cded7f5b04d3ed38","mcp_get_code":{"code_sha256":"cded7f5b04d3ed38"}},{"arxiv_id":"2302.10607","paper":"/paper/differentiable-multi-target-causal-bayesian","title":"Differentiable Multi-Target Causal Bayesian Experimental Design","date":"2023-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yannadani/DiffCBED","path":"models/dag_bootstrap.py","file_url":"https://github.com/yannadani/DiffCBED/blob/HEAD/models/dag_bootstrap.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b9e95c45f6e0f42","mcp_get_code":{"code_sha256":"0b9e95c45f6e0f42"}},{"arxiv_id":"2206.13687","paper":"/paper/poem-out-of-distribution-detection-with","title":"POEM: Out-of-Distribution Detection with Posterior Sampling","date":"2022-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/hopfield-boosting","path":"hopfield_boosting/trainer.py","file_url":"https://github.com/ml-jku/hopfield-boosting/blob/HEAD/hopfield_boosting/trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad16a03dfc3e43e8","mcp_get_code":{"code_sha256":"ad16a03dfc3e43e8"}},{"arxiv_id":"2203.02016","paper":"/paper/interventions-where-and-how-experimental","title":"Interventions, Where and How? Experimental Design for Causal Models at Scale","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yannadani/cbed","path":"strategies/cbed.py","file_url":"https://github.com/yannadani/cbed/blob/HEAD/strategies/cbed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b9e95c45f6e0f42","mcp_get_code":{"code_sha256":"0b9e95c45f6e0f42"}},{"arxiv_id":"1901.02731","paper":"/paper/a-comprehensive-guide-to-bayesian","title":"A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference","date":"2019-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kumar-shridhar/PyTorch-BayesianCNN","path":"utils.py","file_url":"https://github.com/kumar-shridhar/PyTorch-BayesianCNN/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25d66479cebad409","mcp_get_code":{"code_sha256":"25d66479cebad409"}},{"arxiv_id":"1810.04152","paper":"/paper/doubly-reparameterized-gradient-estimators","title":"Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives","date":"2018-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nbip/IWAE","path":"src/utils.py","file_url":"https://github.com/nbip/IWAE/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d372626c71f42bf0","mcp_get_code":{"code_sha256":"d372626c71f42bf0"}}]}