{"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/weight-norm","entry":"weight_norm","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":14,"n_papers_ran":6,"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":12,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":15,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":3,"unverified":7},"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":"2603.00478","paper":"/paper/arxiv-2603-00478","title":"Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"Frankluox/FewTrans","path":"architectures/classifier/finetune.py","file_url":"https://github.com/Frankluox/FewTrans/blob/HEAD/architectures/classifier/finetune.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2471db1563b59f62","mcp_get_code":{"code_sha256":"2471db1563b59f62"}},{"arxiv_id":"2602.03024","paper":"/paper/arxiv-2602-03024","title":"Consistency Deep Equilibrium Models","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"landrarwolf/CDEQ","path":"CDEQ-src/lib/optimizations.py","file_url":"https://github.com/landrarwolf/CDEQ/blob/HEAD/CDEQ-src/lib/optimizations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4bf00e788db9f349","mcp_get_code":{"code_sha256":"4bf00e788db9f349"}},{"arxiv_id":"2512.23278","paper":"/paper/arxiv-2512-23278","title":"Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for Few-step High-Fidelity Audio Generation","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"k2-fsa/Flow2GAN","path":"flow2gan/models/discriminators.py","file_url":"https://github.com/k2-fsa/Flow2GAN/blob/HEAD/flow2gan/models/discriminators.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":"353e388680850619","mcp_get_code":{"code_sha256":"353e388680850619"}},{"arxiv_id":"2411.00899","paper":"/paper/certified-robustness-for-deep-equilibrium-1","title":"Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WeizhiGao/Serialized-Randomized-Smoothing","path":"DEQ/lib/optimizations.py","file_url":"https://github.com/WeizhiGao/Serialized-Randomized-Smoothing/blob/HEAD/DEQ/lib/optimizations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bf00e788db9f349","mcp_get_code":{"code_sha256":"4bf00e788db9f349"}},{"arxiv_id":"2407.00626","paper":"/paper/maximum-entropy-inverse-reinforcement-1","title":"Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models","date":"2024-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"swyoon/Diffusion-by-MaxEntIRL","path":"models/utils.py","file_url":"https://github.com/swyoon/Diffusion-by-MaxEntIRL/blob/HEAD/models/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ffd7b0f9be1df615","mcp_get_code":{"code_sha256":"ffd7b0f9be1df615"}},{"arxiv_id":"2308.10873","paper":"/paper/spikingbert-distilling-bert-to-train-spiking","title":"SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurocomplab-psu/spikingbert","path":"implicit_bert/modules/optimizations.py","file_url":"https://github.com/neurocomplab-psu/spikingbert/blob/HEAD/implicit_bert/modules/optimizations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f0fda38cdd52aefb","mcp_get_code":{"code_sha256":"f0fda38cdd52aefb"}},{"arxiv_id":"2111.05177","paper":"/paper/on-training-implicit-models","title":"On Training Implicit Models","date":"2021-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsunshine/phantom_grad","path":"MDEQ/MDEQ_ImageNet/modules/optimizations.py","file_url":"https://github.com/gsunshine/phantom_grad/blob/HEAD/MDEQ/MDEQ_ImageNet/modules/optimizations.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0fda38cdd52aefb","mcp_get_code":{"code_sha256":"f0fda38cdd52aefb"}},{"arxiv_id":"2010.16417","paper":"/paper/michigan-multi-input-conditioned-hair-image","title":"MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tzt101/MichiGAN","path":"models/networks/normalization.py","file_url":"https://github.com/tzt101/MichiGAN/blob/HEAD/models/networks/normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c94542e9e790ad7b","mcp_get_code":{"code_sha256":"c94542e9e790ad7b"}},{"arxiv_id":"2007.01628","paper":"/paper/hdr-gan-hdr-image-reconstruction-from-multi","title":"HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions","date":"2020-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nonu116/HDR-GAN","path":"model/ops.py","file_url":"https://github.com/nonu116/HDR-GAN/blob/HEAD/model/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eee2c043144fd19a","mcp_get_code":{"code_sha256":"eee2c043144fd19a"}},{"arxiv_id":"2006.08656","paper":"/paper/multiscale-deep-equilibrium-models","title":"Multiscale Deep Equilibrium Models","date":"2020-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/deq","path":"MDEQ-Vision/lib/models/mdeq_core.py","file_url":"https://github.com/locuslab/deq/blob/HEAD/MDEQ-Vision/lib/models/mdeq_core.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a27206c36724f1c4","mcp_get_code":{"code_sha256":"a27206c36724f1c4"}},{"arxiv_id":"2001.08514","paper":"/paper/filter-sketch-for-network-pruning","title":"Filter Sketch for Network Pruning","date":"2020-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmbxmu/FilterSketch","path":"sketch_cifar.py","file_url":"https://github.com/lmbxmu/FilterSketch/blob/HEAD/sketch_cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6636a7e0eab3af9","mcp_get_code":{"code_sha256":"b6636a7e0eab3af9"}},{"arxiv_id":"2001.01565","paper":"/paper/stance-detection-benchmark-how-robust-is-your","title":"Stance Detection Benchmark: How Robust Is Your Stance Detection?","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/mdl-stance-robustness","path":"module/my_optim.py","file_url":"https://github.com/UKPLab/mdl-stance-robustness/blob/HEAD/module/my_optim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f9a0dbc776dbc60","mcp_get_code":{"code_sha256":"0f9a0dbc776dbc60"}},{"arxiv_id":"1810.06682","paper":"/paper/trellis-networks-for-sequence-modeling","title":"Trellis Networks for Sequence Modeling","date":"2018-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/trellisnet","path":"TrellisNet/optimizations.py","file_url":"https://github.com/locuslab/trellisnet/blob/HEAD/TrellisNet/optimizations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bf00e788db9f349","mcp_get_code":{"code_sha256":"4bf00e788db9f349"}},{"arxiv_id":"1803.01814","paper":"/paper/norm-matters-efficient-and-accurate","title":"Norm matters: efficient and accurate normalization schemes in deep networks","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/norm_matters","path":"models/bwn.py","file_url":"https://github.com/eladhoffer/norm_matters/blob/HEAD/models/bwn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"363938bd90a2ba98","mcp_get_code":{"code_sha256":"363938bd90a2ba98"}},{"arxiv_id":"1803.01814","paper":"/paper/norm-matters-efficient-and-accurate","title":"Norm matters: efficient and accurate normalization schemes in deep networks","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/norm_matters","path":"models/bwn_alt.py","file_url":"https://github.com/eladhoffer/norm_matters/blob/HEAD/models/bwn_alt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4113d38a24b49013","mcp_get_code":{"code_sha256":"4113d38a24b49013"}}]}