{"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/negative-log-likelihood","entry":"negative_log_likelihood","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":6,"n_papers_ran":2,"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":2,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":1,"unverified":4},"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":"2605.06484","paper":"/paper/arxiv-2605-06484","title":"Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"facebookresearch/estimate-level-adjustment","path":"estimate_level_adjustment/adjusted_ppi_estimator.py","file_url":"https://github.com/facebookresearch/estimate-level-adjustment/blob/HEAD/estimate_level_adjustment/adjusted_ppi_estimator.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"e09a30d552ae235c","mcp_get_code":{"code_sha256":"e09a30d552ae235c"}},{"arxiv_id":"2603.18083","paper":"/paper/arxiv-2603-18083","title":"Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"Ratun11/Meta-BayFL","path":"matabayfl/models.py","file_url":"https://github.com/Ratun11/Meta-BayFL/blob/HEAD/matabayfl/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c18ec5de2f7b7401","mcp_get_code":{"code_sha256":"c18ec5de2f7b7401"}},{"arxiv_id":"2410.01086","paper":"/paper/an-introduction-to-deep-survival-analysis","title":"An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"georgehc/survival-intro","path":"ddh/losses.py","file_url":"https://github.com/georgehc/survival-intro/blob/HEAD/ddh/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"69bfc00eeb85b25c","mcp_get_code":{"code_sha256":"69bfc00eeb85b25c"}},{"arxiv_id":"2405.02793","paper":"/paper/imageinwords-unlocking-hyper-detailed-image","title":"ImageInWords: Unlocking Hyper-Detailed Image Descriptions","date":"2024-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-deepmind/proactive_t2i_agents","path":"agent/belief_metrics.py","file_url":"https://github.com/google-deepmind/proactive_t2i_agents/blob/HEAD/agent/belief_metrics.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":"b9dcd4f5f46998b9","mcp_get_code":{"code_sha256":"b9dcd4f5f46998b9"}},{"arxiv_id":"2204.04516","paper":"/paper/uncertainty-informed-deep-learning-models","title":"Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology","date":"2022-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamesdolezal/slideflow","path":"slideflow/model/tensorflow_utils.py","file_url":"https://github.com/jamesdolezal/slideflow/blob/HEAD/slideflow/model/tensorflow_utils.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":"db044050ec123a2d","mcp_get_code":{"code_sha256":"db044050ec123a2d"}},{"arxiv_id":"1606.05328","paper":"/paper/conditional-image-generation-with-pixelcnn","title":"Conditional Image Generation with PixelCNN Decoders","date":"2016-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kkleidal/gatedpixelcnnpytorch","path":"mnist_pixelcnn_train.py","file_url":"https://github.com/kkleidal/gatedpixelcnnpytorch/blob/HEAD/mnist_pixelcnn_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e1fa075299151e23","mcp_get_code":{"code_sha256":"e1fa075299151e23"}}]}