{"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-init","entry":"weight_init","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":73,"n_papers_ran":50,"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":26,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":73,"n_places_pointer_only":37,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":2,"ran":0,"unverified":23},"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":"2608.07870","paper":"/paper/arxiv-2608-07870","title":"V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"Aladoro/Stabilizing-Off-Policy-RL","path":"analysis_modules.py","file_url":"https://github.com/Aladoro/Stabilizing-Off-Policy-RL/blob/HEAD/analysis_modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8f4a0c10af4d7c48","mcp_get_code":{"code_sha256":"8f4a0c10af4d7c48"}},{"arxiv_id":"2605.31596","paper":"/paper/arxiv-2605-31596","title":"KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"voilalab/KLIP","path":"CT/training/networks.py","file_url":"https://github.com/voilalab/KLIP/blob/HEAD/CT/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2605.13054","paper":"/paper/arxiv-2605-13054","title":"Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"BattleWen/IGDF","path":"algorithms/igdf/contrastiveoi.py","file_url":"https://github.com/BattleWen/IGDF/blob/HEAD/algorithms/igdf/contrastiveoi.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cdb5ee1c46cc51d7","mcp_get_code":{"code_sha256":"cdb5ee1c46cc51d7"}},{"arxiv_id":"2605.05520","paper":"/paper/arxiv-2605-05520","title":"Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Badr-MOUFAD/rainfield-diffusion-models","path":"training/_network.py","file_url":"https://github.com/Badr-MOUFAD/rainfield-diffusion-models/blob/HEAD/training/_network.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2605.01242","paper":"/paper/arxiv-2605-01242","title":"Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"shelowize/lvrep-rl","path":"agent/ctrlsac/ctrlsac_agent.py","file_url":"https://github.com/shelowize/lvrep-rl/blob/HEAD/agent/ctrlsac/ctrlsac_agent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c802a836c9dc2e07","mcp_get_code":{"code_sha256":"c802a836c9dc2e07"}},{"arxiv_id":"2604.16044","paper":"/paper/arxiv-2604-16044","title":"Elucidating the SNR-t Bias of Diffusion Probabilistic Models","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"AMAP-ML/DCW","path":"training/networks.py","file_url":"https://github.com/AMAP-ML/DCW/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2604.12668","paper":"/paper/arxiv-2604-12668","title":"OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"atrijhy/OFA-Diffusion_Compression","path":"edm/networks_ofa.py","file_url":"https://github.com/atrijhy/OFA-Diffusion_Compression/blob/HEAD/edm/networks_ofa.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2604.09041","paper":"/paper/arxiv-2604-09041","title":"U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Rose-STL-Lab/u-cast","path":"src/models/networks_edm.py","file_url":"https://github.com/Rose-STL-Lab/u-cast/blob/HEAD/src/models/networks_edm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2604.00307","paper":"/paper/arxiv-2604-00307","title":"SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"slimgroup/SAGE","path":"training/networks.py","file_url":"https://github.com/slimgroup/SAGE/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2602.24240","paper":"/paper/arxiv-2602-24240","title":"Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Blazedengcy/GTASR","path":"basicsr/archs/edm_unet_arch.py","file_url":"https://github.com/Blazedengcy/GTASR/blob/HEAD/basicsr/archs/edm_unet_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2602.09708","paper":"/paper/arxiv-2602-09708","title":"Physics-Informed Diffusion Models in Spectral Space","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"deeplearningmethods/PISD","path":"training/networks.py","file_url":"https://github.com/deeplearningmethods/PISD/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2602.09303","paper":"/paper/arxiv-2602-09303","title":"Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"twMisc/sCM-PINN","path":"darcy/networks.py","file_url":"https://github.com/twMisc/sCM-PINN/blob/HEAD/darcy/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2601.21306","paper":"/paper/arxiv-2601-21306","title":"The Surprising Difficulty of Search in Model-Based Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"facebookresearch/MRSQ","path":"MRSQ/MRSQ.py","file_url":"https://github.com/facebookresearch/MRSQ/blob/HEAD/MRSQ/MRSQ.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"575b4a52d71bbed3","mcp_get_code":{"code_sha256":"575b4a52d71bbed3"}},{"arxiv_id":"2601.00225","paper":"/paper/arxiv-2601-00225","title":"Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Li-aobo/SynDR-IQA","path":"BaseIQASolver.py","file_url":"https://github.com/Li-aobo/SynDR-IQA/blob/HEAD/BaseIQASolver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d5502292190f44a","mcp_get_code":{"code_sha256":"8d5502292190f44a"}},{"arxiv_id":"2512.20233","paper":"/paper/arxiv-2512-20233","title":"How I Met Your Bias: Investigating Bias Amplification in Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"NVlabs/edm","path":"training/networks.py","file_url":"https://github.com/NVlabs/edm/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2512.00252","paper":"/paper/arxiv-2512-00252","title":"DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"Erik-Wikingsson/DAISI","path":"assimilation/methods/daisi.py","file_url":"https://github.com/Erik-Wikingsson/DAISI/blob/HEAD/assimilation/methods/daisi.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18899a598247d1a8","mcp_get_code":{"code_sha256":"18899a598247d1a8"}},{"arxiv_id":"2511.03197","paper":"/paper/arxiv-2511-03197","title":"A Probabilistic U-Net Approach to Downscaling Climate Simulations","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"MaryamAlipourH/prob-unet-climate-downscaling","path":"src/baseline/deterministic_unet.py","file_url":"https://github.com/MaryamAlipourH/prob-unet-climate-downscaling/blob/HEAD/src/baseline/deterministic_unet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2510.09060","paper":"/paper/arxiv-2510-09060","title":"Letting Trajectories Spread: Quality-Preserving Control for Diverse Flow Matching","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"gcorso/particle-guidance","path":"stable_diffusion/training/networks.py","file_url":"https://github.com/gcorso/particle-guidance/blob/HEAD/stable_diffusion/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2510.06699","paper":"/paper/arxiv-2510-06699","title":"A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"azencot-group/ImagenI2R","path":"models/networks.py","file_url":"https://github.com/azencot-group/ImagenI2R/blob/HEAD/models/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2509.24526","paper":"/paper/arxiv-2509-24526","title":"CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"sony/cmt","path":"training/networks.py","file_url":"https://github.com/sony/cmt/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2509.20570","paper":"/paper/arxiv-2509-20570","title":"PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"mingze-yuan/PIRF","path":"training/networks.py","file_url":"https://github.com/mingze-yuan/PIRF/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2509.18190","paper":"/paper/arxiv-2509-18190","title":"HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"cloor/HazeFlow","path":"models/edm_networks.py","file_url":"https://github.com/cloor/HazeFlow/blob/HEAD/models/edm_networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2507.10072","paper":null,"title":"arXiv:2507.10072","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"kunzhan/wpp","path":"EDM-DWT-MM/training/networks.py","file_url":"https://github.com/kunzhan/wpp/blob/HEAD/EDM-DWT-MM/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2506.09376","paper":"/paper/revisiting-diffusion-models-from-generative","title":"Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation","date":"2025-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zyriix/GDD","path":"training/networks.py","file_url":"https://github.com/Zyriix/GDD/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2505.07447","paper":"/paper/unified-continuous-generative-models","title":"Unified Continuous Generative Models","date":"2025-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LINs-Lab/UCGM","path":"networks/unetplus.py","file_url":"https://github.com/LINs-Lab/UCGM/blob/HEAD/networks/unetplus.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2502.18197","paper":"/paper/training-consistency-models-with-variational","title":"Training Consistency Models with Variational Noise Coupling","date":"2025-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sony/vct","path":"networks/edm_networks.py","file_url":"https://github.com/sony/vct/blob/HEAD/networks/edm_networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2502.02538","paper":"/paper/flow-q-learning","title":"Flow Q-Learning","date":"2025-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MohammadrezaNakhaei/FQL","path":"fql.py","file_url":"https://github.com/MohammadrezaNakhaei/FQL/blob/HEAD/fql.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79f09007f6cc0351","mcp_get_code":{"code_sha256":"79f09007f6cc0351"}},{"arxiv_id":"2502.00379","paper":"/paper/latent-action-learning-requires-supervision","title":"Latent Action Learning Requires Supervision in the Presence of Distractors","date":"2025-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dunnolab/laom","path":"src/nn.py","file_url":"https://github.com/dunnolab/laom/blob/HEAD/src/nn.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":"92b3b815b957a079","mcp_get_code":{"code_sha256":"92b3b815b957a079"}},{"arxiv_id":"2501.15785","paper":"/paper/memorization-and-regularization-in-generative","title":"Memorization and Regularization in Generative Diffusion Models","date":"2025-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baptistar/DiffusionModelDynamics","path":"RectangleImages/training/networks.py","file_url":"https://github.com/baptistar/DiffusionModelDynamics/blob/HEAD/RectangleImages/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2412.07808","paper":"/paper/boosting-alignment-for-post-unlearning-text","title":"Boosting Alignment for Post-Unlearning Text-to-Image Generative Models","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"reds-lab/restricted_gradient_diversity_unlearning","path":"CIFAR/training/networks.py","file_url":"https://github.com/reds-lab/restricted_gradient_diversity_unlearning/blob/HEAD/CIFAR/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2411.08378","paper":"/paper/physics-informed-distillation-for-diffusion","title":"Physics Informed Distillation for Diffusion Models","date":"2024-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pantheon5100/pid_diffusion","path":"cm/network.py","file_url":"https://github.com/pantheon5100/pid_diffusion/blob/HEAD/cm/network.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2410.24060","paper":"/paper/understanding-generalizability-of-diffusion","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure","path":"training/networks.py","file_url":"https://github.com/Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2410.19538","paper":"/paper/utilizing-image-transforms-and-diffusion","title":"Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series","date":"2024-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azencot-group/ImagenTime","path":"models/networks.py","file_url":"https://github.com/azencot-group/ImagenTime/blob/HEAD/models/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2407.12718","paper":"/paper/slimflow-training-smaller-one-step-diffusion","title":"SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanzhi-zhu/SlimFlow","path":"models/edm_networks.py","file_url":"https://github.com/yuanzhi-zhu/SlimFlow/blob/HEAD/models/edm_networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2407.09247","paper":"/paper/constrained-intrinsic-motivation-for","title":"Constrained Intrinsic Motivation for Reinforcement Learning","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"x-zheng16/CIM","path":"src/policy/cim.py","file_url":"https://github.com/x-zheng16/CIM/blob/HEAD/src/policy/cim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7219bee4c76bb49a","mcp_get_code":{"code_sha256":"7219bee4c76bb49a"}},{"arxiv_id":"2406.17763","paper":"/paper/diffusionpde-generative-pde-solving-under","title":"DiffusionPDE: Generative PDE-Solving Under Partial Observation","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhhuangchloe/DiffusionPDE","path":"training/networks.py","file_url":"https://github.com/jhhuangchloe/DiffusionPDE/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2405.19690","paper":"/paper/diffusion-policies-creating-a-trust-region","title":"Diffusion Policies creating a Trust Region for Offline Reinforcement Learning","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianyucodings/diffusion_trusted_q_learning","path":"agents/dtql.py","file_url":"https://github.com/tianyucodings/diffusion_trusted_q_learning/blob/HEAD/agents/dtql.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e16f745c22233bb1","mcp_get_code":{"code_sha256":"e16f745c22233bb1"}},{"arxiv_id":"2405.17111","paper":"/paper/diffusion-bridge-autoencoders-for","title":"Diffusion Bridge AutoEncoders for Unsupervised Representation Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aailab-kaist/DBAE","path":"ddbm/edm_unet.py","file_url":"https://github.com/aailab-kaist/DBAE/blob/HEAD/ddbm/edm_unet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2405.16034","paper":"/paper/diffubox-refining-3d-object-detection-with","title":"DiffuBox: Refining 3D Object Detection with Point Diffusion","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cxy1997/DiffuBox","path":"training/networks.py","file_url":"https://github.com/cxy1997/DiffuBox/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2405.14822","paper":"/paper/pagoda-progressive-growing-of-a-one-step","title":"PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sony/pagoda","path":"cm/networks.py","file_url":"https://github.com/sony/pagoda/blob/HEAD/cm/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2404.17752","paper":"/paper/generative-diffusion-based-downscaling-for","title":"Generative Diffusion-based Downscaling for Climate","date":"2024-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robbiewatt1/climatediffuse","path":"src/Network.py","file_url":"https://github.com/robbiewatt1/climatediffuse/blob/HEAD/src/Network.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2404.00847","paper":"/paper/collaborative-learning-of-anomalies-with","title":"Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AnasEmad11/CLAP","path":"src/config/models.py","file_url":"https://github.com/AnasEmad11/CLAP/blob/HEAD/src/config/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-2.0","inline_ok":false,"code_sha256_prefix":"b66ae79e2925402b","mcp_get_code":{"code_sha256":"b66ae79e2925402b"}},{"arxiv_id":"2403.18636","paper":"/paper/a-diffusion-based-generative-equalizer-for","title":"A Diffusion-Based Generative Equalizer for Music Restoration","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eloimoliner/babe2","path":"networks/cqtdiff.py","file_url":"https://github.com/eloimoliner/babe2/blob/HEAD/networks/cqtdiff.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2403.17460","paper":"/paper/building-bridges-across-spatial-and-temporal","title":"Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongrunmin/RefDiff","path":"training/networks_cond_v14.py","file_url":"https://github.com/dongrunmin/RefDiff/blob/HEAD/training/networks_cond_v14.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2403.12510","paper":"/paper/generalized-consistency-trajectory-models-for","title":"Generalized Consistency Trajectory Models for Image Manipulation","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1202kbs/gctm","path":"networks.py","file_url":"https://github.com/1202kbs/gctm/blob/HEAD/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2403.01189","paper":"/paper/training-unbiased-diffusion-models-from","title":"Training Unbiased Diffusion Models From Biased Dataset","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alsdudrla10/TIW-DSM","path":"training/networks.py","file_url":"https://github.com/alsdudrla10/TIW-DSM/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2310.20030","paper":"/paper/scaling-riemannian-diffusion-models","title":"Scaling Riemannian Diffusion Models","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"louaaron/Scaling-Riemannian-Diffusion","path":"contrastive_ood/model.py","file_url":"https://github.com/louaaron/Scaling-Riemannian-Diffusion/blob/HEAD/contrastive_ood/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18899a598247d1a8","mcp_get_code":{"code_sha256":"18899a598247d1a8"}},{"arxiv_id":"2310.19668","paper":"/paper/drm-mastering-visual-reinforcement-learning","title":"DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"premiertaco/premier-taco","path":"premier_taco.py","file_url":"https://github.com/premiertaco/premier-taco/blob/HEAD/premier_taco.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"28ac15d6aea86e0e","mcp_get_code":{"code_sha256":"28ac15d6aea86e0e"}},{"arxiv_id":"2310.06389","paper":"/paper/learning-stackable-and-skippable-lego-bricks","title":"Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JegZheng/LEGODiffusion","path":"training/networks.py","file_url":"https://github.com/JegZheng/LEGODiffusion/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2310.04041","paper":"/paper/observation-guided-diffusion-probabilistic","title":"Observation-Guided Diffusion Probabilistic Models","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junoh-kang/ogdm_edm","path":"training/networks.py","file_url":"https://github.com/junoh-kang/ogdm_edm/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2310.02664","paper":"/paper/on-memorization-in-diffusion-models","title":"On Memorization in Diffusion Models","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/DiffMemorize","path":"training/networks.py","file_url":"https://github.com/sail-sg/DiffMemorize/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2310.02279","paper":"/paper/consistency-trajectory-models-learning","title":"Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion","date":"2023-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kim-Dongjun/ctm-cifar10","path":"cm/networks.py","file_url":"https://github.com/Kim-Dongjun/ctm-cifar10/blob/HEAD/cm/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2309.07867","paper":"/paper/beta-diffusion","title":"Beta Diffusion","date":"2023-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingyuanzhou/Beta-Diffusion","path":"image_experiment/training/networks.py","file_url":"https://github.com/mingyuanzhou/Beta-Diffusion/blob/HEAD/image_experiment/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2309.03350","paper":"/paper/relay-diffusion-unifying-diffusion-process-1","title":"Relay Diffusion: Unifying diffusion process across resolutions for image synthesis","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THUDM/RelayDiffusion","path":"training/networks.py","file_url":"https://github.com/THUDM/RelayDiffusion/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2308.15321","paper":"/paper/elucidating-the-exposure-bias-in-diffusion","title":"Elucidating the Exposure Bias in Diffusion Models","date":"2023-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"forever208/edm-es","path":"training/networks.py","file_url":"https://github.com/forever208/edm-es/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2307.01646","paper":"/paper/swingnn-rethinking-permutation-invariance-in","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiyan98/swingnn","path":"model/unet/unet_edm.py","file_url":"https://github.com/qiyan98/swingnn/blob/HEAD/model/unet/unet_edm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2306.06991","paper":"/paper/fast-diffusion-model","title":"Fast Diffusion Model","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/fdm","path":"training/networks.py","file_url":"https://github.com/sail-sg/fdm/blob/HEAD/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2305.16269","paper":"/paper/udpm-upsampling-diffusion-probabilistic","title":"UDPM: Upsampling Diffusion Probabilistic Models","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shadyabh/udpm","path":"EDM_nets.py","file_url":"https://github.com/shadyabh/udpm/blob/HEAD/EDM_nets.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2305.15266","paper":"/paper/diffusion-based-audio-inpainting","title":"Diffusion-Based Audio Inpainting","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eloimoliner/audio-inpainting-diffusion","path":"networks/unet_cqt_oct_with_projattention_adaLN_2.py","file_url":"https://github.com/eloimoliner/audio-inpainting-diffusion/blob/HEAD/networks/unet_cqt_oct_with_projattention_adaLN_2.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2305.08293","paper":"/paper/identity-preserving-talking-face-generation","title":"Identity-Preserving Talking Face Generation with Landmark and Appearance Priors","date":"2023-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Weizhi-Zhong/IP_LAP","path":"models/landmark_generator.py","file_url":"https://github.com/Weizhi-Zhong/IP_LAP/blob/HEAD/models/landmark_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":"4d77c95665872e53","mcp_get_code":{"code_sha256":"4d77c95665872e53"}},{"arxiv_id":"2305.05797","paper":"/paper/fully-bayesian-vib-deepssm","title":"Fully Bayesian VIB-DeepSSM","date":"2023-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jadie1/bvib-deepssm","path":"trainer.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f339ab5634d8fce6","mcp_get_code":{"code_sha256":"f339ab5634d8fce6"}},{"arxiv_id":"2305.04477","paper":"/paper/behavior-contrastive-learning-for","title":"Behavior Contrastive Learning for Unsupervised Skill Discovery","date":"2023-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rooshy-yang/becl","path":"agent/becl.py","file_url":"https://github.com/rooshy-yang/becl/blob/HEAD/agent/becl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d7fab6117dbc6a8","mcp_get_code":{"code_sha256":"2d7fab6117dbc6a8"}},{"arxiv_id":"2303.10137","paper":"/paper/a-recipe-for-watermarking-diffusion-models","title":"A Recipe for Watermarking Diffusion Models","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunqing-me/watermarkdm","path":"edm/training/networks.py","file_url":"https://github.com/yunqing-me/watermarkdm/blob/HEAD/edm/training/networks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d41a4250066bce93","mcp_get_code":{"code_sha256":"d41a4250066bce93"}},{"arxiv_id":"2301.04104","paper":"/paper/mastering-diverse-domains-through-world","title":"Mastering Diverse Domains through World Models","date":"2023-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nm512/dreamerv3-torch","path":"dreamer.py","file_url":"https://github.com/nm512/dreamerv3-torch/blob/HEAD/dreamer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9aaedf6b6e636015","mcp_get_code":{"code_sha256":"9aaedf6b6e636015"}},{"arxiv_id":"2210.13011","paper":"/paper/on-all-action-policy-gradients","title":"On Many-Actions Policy Gradient","date":"2022-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"papersubmissions-anon/daa-ppo","path":"ppo_utils.py","file_url":"https://github.com/papersubmissions-anon/daa-ppo/blob/HEAD/ppo_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a63e89d67417081","mcp_get_code":{"code_sha256":"4a63e89d67417081"}},{"arxiv_id":"2210.10765","paper":"/paper/when-to-ask-for-help-proactive-interventions","title":"When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tajwarfahim/proactive_interventions","path":"paint/agents.py","file_url":"https://github.com/tajwarfahim/proactive_interventions/blob/HEAD/paint/agents.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ae2c2750948ff392","mcp_get_code":{"code_sha256":"ae2c2750948ff392"}},{"arxiv_id":"2203.03535","paper":"/paper/influencing-long-term-behavior-in-multiagent","title":"Influencing Long-Term Behavior in Multiagent Reinforcement Learning","date":"2022-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dkkim93/further","path":"algorithm/further/agent.py","file_url":"https://github.com/dkkim93/further/blob/HEAD/algorithm/further/agent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb1c431c7a635b3f","mcp_get_code":{"code_sha256":"cb1c431c7a635b3f"}},{"arxiv_id":"2103.14635","paper":"/paper/paconv-position-adaptive-convolution-with","title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","date":"2021-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CVMI-Lab/PAConv","path":"scene_seg/model/pointnet2/paconv.py","file_url":"https://github.com/CVMI-Lab/PAConv/blob/HEAD/scene_seg/model/pointnet2/paconv.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":"0db082ffd68d9baa","mcp_get_code":{"code_sha256":"0db082ffd68d9baa"}},{"arxiv_id":"2007.04309","paper":"/paper/self-supervised-policy-adaptation-during","title":"Self-Supervised Policy Adaptation during Deployment","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicklashansen/policy-adaptation-during-deployment","path":"src/agent/agent.py","file_url":"https://github.com/nicklashansen/policy-adaptation-during-deployment/blob/HEAD/src/agent/agent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48b3228cfe8d98ae","mcp_get_code":{"code_sha256":"48b3228cfe8d98ae"}},{"arxiv_id":"2003.00651","paper":"/paper/global-context-aware-progressive-aggregation","title":"Global Context-Aware Progressive Aggregation Network for Salient Object Detection","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JosephChenHub/GCPANet","path":"net.py","file_url":"https://github.com/JosephChenHub/GCPANet/blob/HEAD/net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b015328b53e28cc0","mcp_get_code":{"code_sha256":"b015328b53e28cc0"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaminYeasar/off_policy_ac","path":"SAC/SAC.py","file_url":"https://github.com/SaminYeasar/off_policy_ac/blob/HEAD/SAC/SAC.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"68c08ef9d5d473a7","mcp_get_code":{"code_sha256":"68c08ef9d5d473a7"}},{"arxiv_id":"1611.02779","paper":"/paper/rl2-fast-reinforcement-learning-via-slow","title":"RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning","date":"2016-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliengirlliv/teachable","path":"algos/ppo.py","file_url":"https://github.com/aliengirlliv/teachable/blob/HEAD/algos/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a3b5d4c89f6aec7","mcp_get_code":{"code_sha256":"6a3b5d4c89f6aec7"}},{"arxiv_id":"openreview_AAWlum38oE","paper":null,"title":"arXiv:openreview_AAWlum38oE","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"1202kbs/AYT","path":"src/ayt/unets.py","file_url":"https://github.com/1202kbs/AYT/blob/HEAD/src/ayt/unets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d92d2499830e3ea","mcp_get_code":{"code_sha256":"1d92d2499830e3ea"}}]}