{"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/get","entry":"get","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":42,"n_papers_ran":8,"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":46,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":48,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":3,"unverified":39},"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":"2607.28666","paper":"/paper/arxiv-2607-28666","title":"The Checking Problem What must be true before AI ships in a regulated firm","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"dsauce/checking-problem","path":"eval/fetch_public_corpus.py","file_url":"https://github.com/dsauce/checking-problem/blob/HEAD/eval/fetch_public_corpus.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"8fed70d7e57dbda3","mcp_get_code":{"code_sha256":"8fed70d7e57dbda3"}},{"arxiv_id":"2605.29796","paper":"/paper/arxiv-2605-29796","title":"SAAS: Self-Aware Reinforcement Learning for Over-Search Mitigation in Agentic Search","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"XMUDeepLIT/SAAS","path":"search/generate_with_search.py","file_url":"https://github.com/XMUDeepLIT/SAAS/blob/HEAD/search/generate_with_search.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4269342bd0c429b8","mcp_get_code":{"code_sha256":"4269342bd0c429b8"}},{"arxiv_id":"2605.26248","paper":"/paper/arxiv-2605-26248","title":"Unified Neural Scaling Laws","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"jax-ml/jax","path":"jax/_src/cache_key.py","file_url":"https://github.com/jax-ml/jax/blob/HEAD/jax/_src/cache_key.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":"9fc0e9452ec7806b","mcp_get_code":{"code_sha256":"9fc0e9452ec7806b"}},{"arxiv_id":"2605.23918","paper":"/paper/arxiv-2605-23918","title":"The Model Parking Tax: Quantifying the Hidden Energy Cost of Always-On GPU Model Deployment","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"8bitai/gpu-parking-tax","path":"analysis/generate_new_experiments_figures.py","file_url":"https://github.com/8bitai/gpu-parking-tax/blob/HEAD/analysis/generate_new_experiments_figures.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"82cf6b4e2ef0030f","mcp_get_code":{"code_sha256":"82cf6b4e2ef0030f"}},{"arxiv_id":"2602.21257","paper":"/paper/arxiv-2602-21257","title":"Structured Prompt Language: Declarative Context Management for LLMs","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"digital-duck/SPL-flow","path":"src/config.py","file_url":"https://github.com/digital-duck/SPL-flow/blob/HEAD/src/config.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":"751c1a023abf1e00","mcp_get_code":{"code_sha256":"751c1a023abf1e00"}},{"arxiv_id":"2602.01772","paper":"/paper/arxiv-2602-01772","title":"DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"YuAirLab/Alpha-Tri","path":"Prosit/losses.py","file_url":"https://github.com/YuAirLab/Alpha-Tri/blob/HEAD/Prosit/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1d496623cc76746","mcp_get_code":{"code_sha256":"e1d496623cc76746"}},{"arxiv_id":"2509.26429","paper":"/paper/arxiv-2509-26429","title":"An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"EmilJavurek/Orthogonal-Q-in-MDPs","path":"algos/registry.py","file_url":"https://github.com/EmilJavurek/Orthogonal-Q-in-MDPs/blob/HEAD/algos/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbe2abe344b5ef99","mcp_get_code":{"code_sha256":"bbe2abe344b5ef99"}},{"arxiv_id":"2502.12052","paper":"/paper/a-dual-perspective-nlg-meta-evaluation","title":"A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PKU-ONELab/NLG-DualEval","path":"module/data_process.py","file_url":"https://github.com/PKU-ONELab/NLG-DualEval/blob/HEAD/module/data_process.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1fdb9eccea3d1f4","mcp_get_code":{"code_sha256":"c1fdb9eccea3d1f4"}},{"arxiv_id":"2411.06225","paper":"/paper/randnet-parareal-a-time-parallel-pde-solver","title":"RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks","date":"2024-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Parallel-in-Time-Differential-Equations/RandNet-Parareal","path":"Burgers_analysis.py","file_url":"https://github.com/Parallel-in-Time-Differential-Equations/RandNet-Parareal/blob/HEAD/Burgers_analysis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27d1ca21903aa88c","mcp_get_code":{"code_sha256":"27d1ca21903aa88c"}},{"arxiv_id":"2411.03604","paper":"/paper/temporal-difference-learning-using","title":"Temporal-Difference Learning Using Distributed Error Signals","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/jax","path":"jax/_src/cache_key.py","file_url":"https://github.com/google/jax/blob/HEAD/jax/_src/cache_key.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":"9fc0e9452ec7806b","mcp_get_code":{"code_sha256":"9fc0e9452ec7806b"}},{"arxiv_id":"2407.01163","paper":"/paper/benchmarking-predictive-coding-networks-made","title":"Benchmarking Predictive Coding Networks -- Made Simple","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liukidar/pcax","path":"pcx/core/_parameter.py","file_url":"https://github.com/liukidar/pcax/blob/HEAD/pcx/core/_parameter.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ef147485fa96660c","mcp_get_code":{"code_sha256":"ef147485fa96660c"}},{"arxiv_id":"2406.19708","paper":"/paper/a-differentiable-approach-to-multi-scale","title":"A Differentiable Approach to Multi-scale Brain Modeling","date":"2024-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brainpy/BrainPy","path":"brainpy/algorithms/online.py","file_url":"https://github.com/brainpy/BrainPy/blob/HEAD/brainpy/algorithms/online.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"f4bef671e4a4f580","mcp_get_code":{"code_sha256":"f4bef671e4a4f580"}},{"arxiv_id":"2312.15731","paper":"/paper/adaptive-fss-a-novel-few-shot-segmentation","title":"Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingw193/adaptive_fss","path":"model/FPTrans_AdaptiveFSS/losses.py","file_url":"https://github.com/jingw193/adaptive_fss/blob/HEAD/model/FPTrans_AdaptiveFSS/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"109ac9594e4ff15b","mcp_get_code":{"code_sha256":"109ac9594e4ff15b"}},{"arxiv_id":"2311.15100","paper":"/paper/unbalancedness-in-neural-monge-maps-improves","title":"Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation","date":"2023-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ExplainableML/uot-fm","path":"models/inception.py","file_url":"https://github.com/ExplainableML/uot-fm/blob/HEAD/models/inception.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c98a6cf41bf8d8ac","mcp_get_code":{"code_sha256":"c98a6cf41bf8d8ac"}},{"arxiv_id":"2305.07017","paper":"/paper/an-inverse-scaling-law-for-clip-training-1","title":"An Inverse Scaling Law for CLIP Training","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UCSC-VLAA/CLIPA","path":"clipa_jax/datasets/core.py","file_url":"https://github.com/UCSC-VLAA/CLIPA/blob/HEAD/clipa_jax/datasets/core.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":"da654725d3df8ece","mcp_get_code":{"code_sha256":"da654725d3df8ece"}},{"arxiv_id":"2205.01580","paper":"/paper/better-plain-vit-baselines-for-imagenet-1k","title":"Better plain ViT baselines for ImageNet-1k","date":"2022-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuyangshu/retinavit","path":"big_vision/datasets/core.py","file_url":"https://github.com/yuyangshu/retinavit/blob/HEAD/big_vision/datasets/core.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":"f40ba1fc76a56621","mcp_get_code":{"code_sha256":"f40ba1fc76a56621"}},{"arxiv_id":"2203.16804","paper":"/paper/brio-bringing-order-to-abstractive","title":"BRIO: Bringing Order to Abstractive Summarization","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"webis-de/summary-workbench","path":"summary-workbench.py","file_url":"https://github.com/webis-de/summary-workbench/blob/HEAD/summary-workbench.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a789693bf4f1d0a2","mcp_get_code":{"code_sha256":"a789693bf4f1d0a2"}},{"arxiv_id":"2201.11528","paper":"/paper/beyond-imagenet-attack-towards-crafting-1","title":"Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains","date":"2022-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alibaba-AAIG/Beyond-ImageNet-Attack","path":"imagenet/dataset.py","file_url":"https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/HEAD/imagenet/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c89d1e59cc2475a1","mcp_get_code":{"code_sha256":"c89d1e59cc2475a1"}},{"arxiv_id":"2110.04593","paper":"/paper/flattening-sharpness-for-dynamic-gradient","title":"Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning","date":"2021-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danruod/fs-dgpm","path":"dataloaders/cifar100.py","file_url":"https://github.com/danruod/fs-dgpm/blob/HEAD/dataloaders/cifar100.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1514ac52326e4f5","mcp_get_code":{"code_sha256":"a1514ac52326e4f5"}},{"arxiv_id":"2110.04593","paper":"/paper/flattening-sharpness-for-dynamic-gradient","title":"Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning","date":"2021-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danruod/fs-dgpm","path":"dataloaders/cifar100_superclass.py","file_url":"https://github.com/danruod/fs-dgpm/blob/HEAD/dataloaders/cifar100_superclass.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af0c633c5c94ca87","mcp_get_code":{"code_sha256":"af0c633c5c94ca87"}},{"arxiv_id":"2106.00572","paper":"/paper/prior-enhanced-few-shot-segmentation-with","title":"Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes","date":"2021-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jarvis73/PEMP","path":"core/losses.py","file_url":"https://github.com/Jarvis73/PEMP/blob/HEAD/core/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f1d68bb61af0511","mcp_get_code":{"code_sha256":"7f1d68bb61af0511"}},{"arxiv_id":"2105.08683","paper":"/paper/learning-embeddings-from-knowledge-graphs","title":"Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes","date":"2021-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Accenture/AmpliGraph","path":"ampligraph/latent_features/loss_functions.py","file_url":"https://github.com/Accenture/AmpliGraph/blob/HEAD/ampligraph/latent_features/loss_functions.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":"e5cdd5cb67b2cb72","mcp_get_code":{"code_sha256":"e5cdd5cb67b2cb72"}},{"arxiv_id":"2105.00266","paper":"/paper/data-driven-discovery-of-physical-laws-with","title":"Data-driven discovery of Green's functions with human-understandable deep learning","date":"2021-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NBoulle/greenlearning","path":"greenlearning/activations.py","file_url":"https://github.com/NBoulle/greenlearning/blob/HEAD/greenlearning/activations.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":"9029e3bf7909c0cf","mcp_get_code":{"code_sha256":"9029e3bf7909c0cf"}},{"arxiv_id":"2103.16457","paper":"/paper/dream-a-fluid-kinetic-framework-for-tokamak","title":"DREAM: a fluid-kinetic framework for tokamak disruption runaway electron simulations","date":null,"month_inferred_from_arxiv_id":"2021-03","title_source":"archive","repo":"chalmersplasmatheory/DREAM","path":"py/DREAM/GeriMap.py","file_url":"https://github.com/chalmersplasmatheory/DREAM/blob/HEAD/py/DREAM/GeriMap.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd3644eb16140a86","mcp_get_code":{"code_sha256":"fd3644eb16140a86"}},{"arxiv_id":"2008.11364","paper":"/paper/benchmarking-semi-supervised-federated","title":"Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models","date":"2020-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhcknzzm/SSFL-Benchmarking-Semi-supervised-Federated-Learning","path":"models/cifar.py","file_url":"https://github.com/jhcknzzm/SSFL-Benchmarking-Semi-supervised-Federated-Learning/blob/HEAD/models/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8be03d81632009c","mcp_get_code":{"code_sha256":"f8be03d81632009c"}},{"arxiv_id":"2008.05570","paper":"/paper/generating-person-scene-interactions-in-3d","title":"PLACE: Proximity Learning of Articulation and Contact in 3D Environments","date":"2020-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanweiliti/PLACE","path":"chamfer_pytorch/get_chamfer.py","file_url":"https://github.com/sanweiliti/PLACE/blob/HEAD/chamfer_pytorch/get_chamfer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a947f54a81b11553","mcp_get_code":{"code_sha256":"a947f54a81b11553"}},{"arxiv_id":"2002.04745","paper":"/paper/on-layer-normalization-in-the-transformer-1","title":"On Layer Normalization in the Transformer Architecture","date":"2020-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"colorfulscoop/tfdlg","path":"tfdlg/activations.py","file_url":"https://github.com/colorfulscoop/tfdlg/blob/HEAD/tfdlg/activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1a80ebe76a998e9","mcp_get_code":{"code_sha256":"c1a80ebe76a998e9"}},{"arxiv_id":"1911.03688","paper":"/paper/convert-efficient-and-accurate-conversational","title":"ConveRT: Efficient and Accurate Conversational Representations from Transformers","date":"2019-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"koujm/convert-tf","path":"model/activations.py","file_url":"https://github.com/koujm/convert-tf/blob/HEAD/model/activations.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":"dc1b23db6eb4a793","mcp_get_code":{"code_sha256":"dc1b23db6eb4a793"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/dataloaders/cifar.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/dataloaders/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f497ffa094e5187d","mcp_get_code":{"code_sha256":"f497ffa094e5187d"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/dataloaders/mixture.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/dataloaders/mixture.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c141139ade360683","mcp_get_code":{"code_sha256":"c141139ade360683"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/dataloaders/mnist2.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/dataloaders/mnist2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b6d1b5f5705f3d7","mcp_get_code":{"code_sha256":"8b6d1b5f5705f3d7"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/dataloaders/mnist5.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/dataloaders/mnist5.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da97457bd98cf9b8","mcp_get_code":{"code_sha256":"da97457bd98cf9b8"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/dataloaders/pmnist.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/dataloaders/pmnist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"690c5890415e69c3","mcp_get_code":{"code_sha256":"690c5890415e69c3"}},{"arxiv_id":"1905.11722","paper":"/paper/a-graph-theoretic-framework-of-recomputation","title":"A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pfnet-research/recompute","path":"scripts/run_schedule.py","file_url":"https://github.com/pfnet-research/recompute/blob/HEAD/scripts/run_schedule.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5e74d7a6d75a42be","mcp_get_code":{"code_sha256":"5e74d7a6d75a42be"}},{"arxiv_id":"1810.09538","paper":"/paper/pyro-deep-universal-probabilistic-programming","title":"Pyro: Deep Universal Probabilistic Programming","date":"2018-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uber/pyro","path":"pyro/settings.py","file_url":"https://github.com/uber/pyro/blob/HEAD/pyro/settings.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":"6d95513e40539eea","mcp_get_code":{"code_sha256":"6d95513e40539eea"}},{"arxiv_id":"1807.03039","paper":"/paper/glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/glow","path":"demo/model.py","file_url":"https://github.com/openai/glow/blob/HEAD/demo/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48dad1e79064a6b1","mcp_get_code":{"code_sha256":"48dad1e79064a6b1"}},{"arxiv_id":"1807.01544","paper":"/paper/textsnake-a-flexible-representation-for","title":"TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes","date":"2018-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"speakstone/TextSnake","path":"dataset/data_coco.py","file_url":"https://github.com/speakstone/TextSnake/blob/HEAD/dataset/data_coco.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d2f61d806e44b0a6","mcp_get_code":{"code_sha256":"d2f61d806e44b0a6"}},{"arxiv_id":"1806.00088","paper":"/paper/peernets-exploiting-peer-wisdom-against","title":"PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks","date":"2018-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tantara/PeerNets-pytorch","path":"src/mnist/dataset.py","file_url":"https://github.com/tantara/PeerNets-pytorch/blob/HEAD/src/mnist/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e1517f6035c39a9","mcp_get_code":{"code_sha256":"0e1517f6035c39a9"}},{"arxiv_id":"1805.04096","paper":"/paper/fighting-fake-news-image-splice-detection-via","title":"Fighting Fake News: Image Splice Detection via Learned Self-Consistency","date":"2018-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minyoungg/selfconsistency","path":"lib/utils/util.py","file_url":"https://github.com/minyoungg/selfconsistency/blob/HEAD/lib/utils/util.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":"0069e4691c451107","mcp_get_code":{"code_sha256":"0069e4691c451107"}},{"arxiv_id":"1803.07416","paper":"/paper/tensor2tensor-for-neural-machine-translation","title":"Tensor2Tensor for Neural Machine Translation","date":"2018-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiachenMao/TransfornerPrune","path":"tensor2tensor/layers/bayes.py","file_url":"https://github.com/JiachenMao/TransfornerPrune/blob/HEAD/tensor2tensor/layers/bayes.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":"30733be1f346d1da","mcp_get_code":{"code_sha256":"30733be1f346d1da"}},{"arxiv_id":"1803.06585","paper":"/paper/learning-long-term-dependencies-via-fourier","title":"Learning Long Term Dependencies via Fourier Recurrent Units","date":"2018-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"limbo018/FRU","path":"code/tflearn/tflearn/losses.py","file_url":"https://github.com/limbo018/FRU/blob/HEAD/code/tflearn/tflearn/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce9eb5970f8c4332","mcp_get_code":{"code_sha256":"ce9eb5970f8c4332"}},{"arxiv_id":"1803.06585","paper":"/paper/learning-long-term-dependencies-via-fourier","title":"Learning Long Term Dependencies via Fourier Recurrent Units","date":"2018-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"limbo018/FRU","path":"code/tflearn/tflearn/activations.py","file_url":"https://github.com/limbo018/FRU/blob/HEAD/code/tflearn/tflearn/activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c0db7052f4bb265","mcp_get_code":{"code_sha256":"2c0db7052f4bb265"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"enhuiz/torchnmt","path":"torchnmt/networks/transformer.py","file_url":"https://github.com/enhuiz/torchnmt/blob/HEAD/torchnmt/networks/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d77f152b7ffa2e14","mcp_get_code":{"code_sha256":"d77f152b7ffa2e14"}},{"arxiv_id":"1705.02304","paper":"/paper/deep-speaker-an-end-to-end-neural-speaker","title":"Deep Speaker: an End-to-End Neural Speaker Embedding System","date":"2017-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prajual/Deep_Speaker","path":"models.py","file_url":"https://github.com/prajual/Deep_Speaker/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2cbf4ce7e0235a0d","mcp_get_code":{"code_sha256":"2cbf4ce7e0235a0d"}},{"arxiv_id":"1609.03499","paper":"/paper/wavenet-a-generative-model-for-raw-audio","title":"WaveNet: A Generative Model for Raw Audio","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rampage644/wavenet","path":"server.py","file_url":"https://github.com/rampage644/wavenet/blob/HEAD/server.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":"1b7121b9a228766d","mcp_get_code":{"code_sha256":"1b7121b9a228766d"}},{"arxiv_id":"1512.00567","paper":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matthias-wright/jax-fid","path":"jax_fid/utils.py","file_url":"https://github.com/matthias-wright/jax-fid/blob/HEAD/jax_fid/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c98a6cf41bf8d8ac","mcp_get_code":{"code_sha256":"c98a6cf41bf8d8ac"}},{"arxiv_id":"Apolinario_CODE-CL_Conceptor-Based_Gradient_Projection_for_Deep_Continual_Learning_ICCV_2025_paper","paper":null,"title":"arXiv:Apolinario_CODE-CL_Conceptor-Based_Gradient_Projection_for_Deep_Continual_Learning_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mapolinario94/CODE-CL","path":"utils/cifar100.py","file_url":"https://github.com/mapolinario94/CODE-CL/blob/HEAD/utils/cifar100.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee437b2433f27166","mcp_get_code":{"code_sha256":"ee437b2433f27166"}},{"arxiv_id":"2022.emnlp-demos.40","paper":null,"title":"arXiv:2022.emnlp-demos.40","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"OpenBMB/BMTrain","path":"bmtrain/debug.py","file_url":"https://github.com/OpenBMB/BMTrain/blob/HEAD/bmtrain/debug.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":"210cf4c28eb1b0c6","mcp_get_code":{"code_sha256":"210cf4c28eb1b0c6"}}]}