{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/denoising/papers/29","list_of":"/task/denoising","task":"Denoising","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":29,"pages_in_order":73,"rows_per_page":100,"rows":[2801,2900],"of":7282,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/denoising","prev":"/task/denoising/papers/28","next":"/task/denoising/papers/30","papers":[{"url":"/paper/an-extended-framework-for-marginalized-domain","slug":"an-extended-framework-for-marginalized-domain","title":"An Extended Framework for Marginalized Domain Adaptation","date":"2017-02-20","arxiv_id":"1702.05993","repositories_listed":1,"syntology":null},{"url":"/paper/dirty-pixels-optimizing-image-classification","slug":"dirty-pixels-optimizing-image-classification","title":"Dirty Pixels: Towards End-to-End Image Processing and Perception","date":"2017-01-23","arxiv_id":"1701.06487","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dirty-pixels-optimizing-image-classification#ran","syntology_url":"https://syntology.ai/paper/1701.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.06487"}},"official":{"repos":["princeton-computational-imaging/DirtyPixels"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-class-aware-denoising","slug":"deep-class-aware-denoising","title":"Deep Class Aware Denoising","date":"2017-01-06","arxiv_id":"1701.01698","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-deep-image-to-image-regression","slug":"generalized-deep-image-to-image-regression","title":"Generalized Deep Image to Image Regression","date":"2016-12-10","arxiv_id":"1612.03268","repositories_listed":1,"syntology":null},{"url":"/paper/on-demand-learning-for-deep-image-restoration","slug":"on-demand-learning-for-deep-image-restoration","title":"On-Demand Learning for Deep Image Restoration","date":"2016-12-05","arxiv_id":"1612.01380","repositories_listed":1,"syntology":null},{"url":"/paper/joint-visual-denoising-and-classification","slug":"joint-visual-denoising-and-classification","title":"Joint Visual Denoising and Classification using Deep Learning","date":"2016-12-04","arxiv_id":"1612.01075","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-representations-using","slug":"learning-deep-representations-using","title":"Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections","date":"2016-11-28","arxiv_id":"1611.09119","repositories_listed":1,"syntology":null},{"url":"/paper/straight-to-shapes-real-time-detection-of","slug":"straight-to-shapes-real-time-detection-of","title":"Straight to Shapes: Real-time Detection of Encoded Shapes","date":"2016-11-23","arxiv_id":"1611.07932","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-deep-residual-learning-for-image","slug":"beyond-deep-residual-learning-for-image","title":"Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification","date":"2016-11-19","arxiv_id":"1611.06345","repositories_listed":1,"syntology":null},{"url":"/paper/neural-based-noise-filtering-from-word","slug":"neural-based-noise-filtering-from-word","title":"Neural-based Noise Filtering from Word Embeddings","date":"2016-10-06","arxiv_id":"1610.01874","repositories_listed":1,"syntology":null},{"url":"/paper/recursive-nearest-agglomeration-rena-fast","slug":"recursive-nearest-agglomeration-rena-fast","title":"Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals","date":"2016-09-15","arxiv_id":"1609.04608","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-on-the-effects-of","slug":"an-empirical-study-on-the-effects-of","title":"An empirical study on the effects of different types of noise in image classification tasks","date":"2016-09-09","arxiv_id":"1609.02781","repositories_listed":1,"syntology":null},{"url":"/paper/convexified-convolutional-neural-networks","slug":"convexified-convolutional-neural-networks","title":"Convexified Convolutional Neural Networks","date":"2016-09-04","arxiv_id":"1609.01000","repositories_listed":1,"syntology":null},{"url":"/paper/non-local-spatial-and-angular-matching","slug":"non-local-spatial-and-angular-matching","title":"Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising","date":"2016-06-23","arxiv_id":"1606.07239","repositories_listed":1,"syntology":null},{"url":"/paper/vconv-dae-deep-volumetric-shape-learning","slug":"vconv-dae-deep-volumetric-shape-learning","title":"VConv-DAE: Deep Volumetric Shape Learning Without Object Labels","date":"2016-04-13","arxiv_id":"1604.03755","repositories_listed":1,"syntology":null},{"url":"/paper/sublabel-accurate-convex-relaxation-of","slug":"sublabel-accurate-convex-relaxation-of","title":"Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies","date":"2016-04-07","arxiv_id":"1604.01980","repositories_listed":1,"syntology":null},{"url":"/paper/a-semisupervised-approach-for-language","slug":"a-semisupervised-approach-for-language","title":"A Semisupervised Approach for Language Identification based on Ladder Networks","date":"2016-04-01","arxiv_id":"1604.00317","repositories_listed":1,"syntology":null},{"url":"/paper/audio-word2vec-unsupervised-learning-of-audio","slug":"audio-word2vec-unsupervised-learning-of-audio","title":"Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder","date":"2016-03-03","arxiv_id":"1603.00982","repositories_listed":1,"syntology":null},{"url":"/paper/patch-ordering-as-a-regularization-for","slug":"patch-ordering-as-a-regularization-for","title":"Patch-Ordering as a Regularization for Inverse Problems in Image Processing","date":"2016-02-26","arxiv_id":"1602.08510","repositories_listed":1,"syntology":null},{"url":"/paper/fast-and-effective-l0-gradient-minimization","slug":"fast-and-effective-l0-gradient-minimization","title":"Fast and Effective L0 Gradient Minimization by Region Fusion","date":"2015-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/binding-via-reconstruction-clustering","slug":"binding-via-reconstruction-clustering","title":"Binding via Reconstruction Clustering","date":"2015-11-19","arxiv_id":"1511.06418","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-sum-of-outer-products-dictionary-1","slug":"efficient-sum-of-outer-products-dictionary-1","title":"Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems","date":"2015-11-19","arxiv_id":"1511.06333","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-distributions-with-linearizing","slug":"predicting-distributions-with-linearizing","title":"Predicting distributions with Linearizing Belief Networks","date":"2015-11-17","arxiv_id":"1511.05622","repositories_listed":1,"syntology":null},{"url":"/paper/a-note-on-the-evaluation-of-generative-models","slug":"a-note-on-the-evaluation-of-generative-models","title":"A note on the evaluation of generative models","date":"2015-11-05","arxiv_id":"1511.01844","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-network-edge-probabilities-by","slug":"estimating-network-edge-probabilities-by","title":"Estimating network edge probabilities by neighborhood smoothing","date":"2015-09-29","arxiv_id":"1509.08588","repositories_listed":1,"syntology":null},{"url":"/paper/deepsat-a-learning-framework-for-satellite","slug":"deepsat-a-learning-framework-for-satellite","title":"DeepSat - A Learning framework for Satellite Imagery","date":"2015-09-11","arxiv_id":"1509.03602","repositories_listed":1,"syntology":null},{"url":"/paper/lateral-connections-in-denoising-autoencoders","slug":"lateral-connections-in-denoising-autoencoders","title":"Lateral Connections in Denoising Autoencoders Support Supervised Learning","date":"2015-04-30","arxiv_id":"1504.08215","repositories_listed":1,"syntology":null},{"url":"/paper/interpolating-convex-and-non-convex-tensor","slug":"interpolating-convex-and-non-convex-tensor","title":"Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm","date":"2015-03-18","arxiv_id":"1503.05479","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-autoencoders-for-fast-combinatorial","slug":"denoising-autoencoders-for-fast-combinatorial","title":"Denoising Autoencoders for fast Combinatorial Black Box Optimization","date":"2015-03-06","arxiv_id":"1503.01954","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-autoencoder-with-modulated-lateral","slug":"denoising-autoencoder-with-modulated-lateral","title":"Denoising autoencoder with modulated lateral connections learns invariant representations of natural images","date":"2014-12-22","arxiv_id":"1412.7210","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adversarial-neural-networks","slug":"domain-adversarial-neural-networks","title":"Domain-Adversarial Neural Networks","date":"2014-12-15","arxiv_id":"1412.4446","repositories_listed":1,"syntology":null},{"url":"/paper/from-neural-pca-to-deep-unsupervised-learning","slug":"from-neural-pca-to-deep-unsupervised-learning","title":"From neural PCA to deep unsupervised learning","date":"2014-11-28","arxiv_id":"1411.7783","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-low-rank-models","slug":"generalized-low-rank-models","title":"Generalized Low Rank Models","date":"2014-10-01","arxiv_id":"1410.0342","repositories_listed":1,"syntology":null},{"url":"/paper/renoir-a-dataset-for-real-low-light-image","slug":"renoir-a-dataset-for-real-low-light-image","title":"RENOIR - A Dataset for Real Low-Light Image Noise Reduction","date":"2014-09-29","arxiv_id":"1409.8230","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/renoir-a-dataset-for-real-low-light-image#ran","syntology_url":"https://syntology.ai/paper/1409.8230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1409.8230"}},"official":null}},{"url":"/paper/scheduled-denoising-autoencoders","slug":"scheduled-denoising-autoencoders","title":"Scheduled denoising autoencoders","date":"2014-06-12","arxiv_id":"1406.3269","repositories_listed":1,"syntology":null},{"url":"/paper/learning-parametric-dictionaries-for-graph","slug":"learning-parametric-dictionaries-for-graph","title":"Learning parametric dictionaries for graph signals","date":"2014-01-05","arxiv_id":"1401.0887","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-denoising-auto-encoders-as","slug":"generalized-denoising-auto-encoders-as","title":"Generalized Denoising Auto-Encoders as Generative Models","date":"2013-05-29","arxiv_id":"1305.6663","repositories_listed":1,"syntology":null},{"url":"/paper/no-reference-image-quality-assessment-in-the","slug":"no-reference-image-quality-assessment-in-the","title":"No-Reference Image Quality Assessment in the Spatial Domain","date":"2012-08-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"diffuman4d-4d-consistent-human-view-synthesis","title":"Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models","date":"2025-07-17","arxiv_id":"2507.13344","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastwdm3d-fast-and-accurate-3d-healthy-tissue","title":"fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting","date":"2025-07-17","arxiv_id":"2507.13146","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-guided-diffusion-for-contrastive","title":"Similarity-Guided Diffusion for Contrastive Sequential Recommendation","date":"2025-07-16","arxiv_id":"2507.11866","repositories_listed":0,"syntology":null},{"url":null,"slug":"airllm-diffusion-policy-based-adaptive-lora","title":"AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air","date":"2025-07-15","arxiv_id":"2507.11515","repositories_listed":0,"syntology":null},{"url":null,"slug":"hug-vas-a-hierarchical-nurbs-based-generative","title":"HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing","date":"2025-07-15","arxiv_id":"2507.11474","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-physics-framework-for-optimal","title":"A statistical physics framework for optimal learning","date":"2025-07-10","arxiv_id":"2507.07907","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-the-spatial-hierarchy-coarse-to","title":"Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion","date":"2025-07-08","arxiv_id":"2507.13366","repositories_listed":0,"syntology":null},{"url":null,"slug":"spade-spatial-aware-denoising-network-for","title":"SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning","date":"2025-07-08","arxiv_id":"2507.05798","repositories_listed":0,"syntology":null},{"url":null,"slug":"unconditional-diffusion-for-generative","title":"Unconditional Diffusion for Generative Sequential Recommendation","date":"2025-07-08","arxiv_id":"2507.06121","repositories_listed":0,"syntology":null},{"url":null,"slug":"cot-lized-diffusion-let-s-reinforce-t2i","title":"CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step","date":"2025-07-06","arxiv_id":"2507.04451","repositories_listed":0,"syntology":null},{"url":null,"slug":"freemorph-tuning-free-generalized-image","title":"FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model","date":"2025-07-02","arxiv_id":"2507.01953","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustereo-robust-zero-shot-stereo-matching","title":"RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather","date":"2025-07-02","arxiv_id":"2507.01653","repositories_listed":0,"syntology":null},{"url":null,"slug":"score-based-model-for-low-rank-tensor","title":"Score-Based Model for Low-Rank Tensor Recovery","date":"2025-06-27","arxiv_id":"2506.22295","repositories_listed":0,"syntology":null},{"url":"/paper/from-cradle-to-cane-a-two-pass-framework-for","slug":"from-cradle-to-cane-a-two-pass-framework-for","title":"From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging","date":"2025-06-26","arxiv_id":"2506.20977","repositories_listed":0,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/from-cradle-to-cane-a-two-pass-framework-for#ran","syntology_url":"https://syntology.ai/paper/2506.20977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20977"}},"official":null}},{"url":null,"slug":"integrating-vehicle-acoustic-data-for","title":"Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou","date":"2025-06-26","arxiv_id":"2506.21269","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-physics-informed-zero-shot","title":"Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising","date":"2025-06-26","arxiv_id":"2506.21499","repositories_listed":0,"syntology":null},{"url":null,"slug":"smoothsinger-a-conditional-diffusion-model","title":"SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture","date":"2025-06-26","arxiv_id":"2506.21478","repositories_listed":0,"syntology":null},{"url":"/paper/ctrl-z-sampling-diffusion-sampling-with","slug":"ctrl-z-sampling-diffusion-sampling-with","title":"Ctrl-Z Sampling: Diffusion Sampling with Controlled Random Zigzag Explorations","date":"2025-06-25","arxiv_id":"2506.20294","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/ctrl-z-sampling-diffusion-sampling-with#ran","syntology_url":"https://syntology.ai/paper/2506.20294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.20294"}},"official":null}},{"url":null,"slug":"tdir-transformer-based-diffusion-for-image","title":"TDiR: Transformer based Diffusion for Image Restoration Tasks","date":"2025-06-25","arxiv_id":"2506.20302","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-surrogates-from-energy-consumers","title":"Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios","date":"2025-06-25","arxiv_id":"2506.20253","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-variable-quantum-diffusion-model","title":"Continuous-variable Quantum Diffusion Model for State Generation and Restoration","date":"2025-06-24","arxiv_id":"2506.19270","repositories_listed":0,"syntology":null},{"url":null,"slug":"naada-a-noise-aware-attention-denoising","title":"NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs","date":"2025-06-24","arxiv_id":"2506.19387","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-averaging-accurately-distills-manifold","title":"Local Averaging Accurately Distills Manifold Structure From Noisy Data","date":"2025-06-23","arxiv_id":"2506.18761","repositories_listed":0,"syntology":null},{"url":null,"slug":"ehcube4p-learning-epistatic-patterns-through","title":"EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation","date":"2025-06-20","arxiv_id":"2506.16921","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-strategies-for-personalized-1","title":"Exploring Strategies for Personalized Radiation Therapy Part II Predicting Tumor Drift Patterns with Diffusion Models","date":"2025-06-20","arxiv_id":"2506.17491","repositories_listed":0,"syntology":null},{"url":null,"slug":"ednet-a-distortion-agnostic-speech","title":"EDNet: A Distortion-Agnostic Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training","date":"2025-06-19","arxiv_id":"2506.16231","repositories_listed":0,"syntology":null},{"url":null,"slug":"flowram-grounding-flow-matching-policy-with-1","title":"FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation","date":"2025-06-19","arxiv_id":"2506.16201","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-scale-spatial-frequency","title":"Learning Multi-scale Spatial-frequency Features for Image Denoising","date":"2025-06-19","arxiv_id":"2506.16307","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-real-time-endoscopic-image-denoising-system","title":"A Real-time Endoscopic Image Denoising System","date":"2025-06-18","arxiv_id":"2506.15395","repositories_listed":0,"syntology":null},{"url":null,"slug":"cwgan-gp-augmented-cae-for-jamming-detection","title":"CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets","date":"2025-06-18","arxiv_id":"2506.15075","repositories_listed":0,"syntology":null},{"url":null,"slug":"fiber-signal-denoising-algorithm-using-hybrid","title":"Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks","date":"2025-06-18","arxiv_id":"2506.15125","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-thermodynamic-computing","title":"Generative thermodynamic computing","date":"2025-06-18","arxiv_id":"2506.15121","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-evaluation-of-deep-learning-1","title":"A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments","date":"2025-06-17","arxiv_id":"2506.15000","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-aware-routing-for-efficient-text-to","title":"Cost-Aware Routing for Efficient Text-To-Image Generation","date":"2025-06-17","arxiv_id":"2506.14753","repositories_listed":0,"syntology":null},{"url":null,"slug":"demonstrating-superresolution-in-radar-range","title":"Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder","date":"2025-06-17","arxiv_id":"2506.14906","repositories_listed":0,"syntology":null},{"url":"/paper/diffusionblocks-blockwise-training-for","slug":"diffusionblocks-blockwise-training-for","title":"DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion","date":"2025-06-17","arxiv_id":"2506.14202","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffusionblocks-blockwise-training-for#ran","syntology_url":"https://syntology.ai/paper/2506.14202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14202"}},"official":null}},{"url":null,"slug":"exploring-diffusion-with-test-time-training","title":"Exploring Diffusion with Test-Time Training on Efficient Image Restoration","date":"2025-06-17","arxiv_id":"2506.14541","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-based-image-restoration-under","title":"Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits","date":"2025-06-17","arxiv_id":"2506.14624","repositories_listed":0,"syntology":null},{"url":null,"slug":"block-wise-adaptive-caching-for-accelerating","title":"Block-wise Adaptive Caching for Accelerating Diffusion Policy","date":"2025-06-16","arxiv_id":"2506.13456","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolvable-conditional-diffusion","title":"Evolvable Conditional Diffusion","date":"2025-06-16","arxiv_id":"2506.13834","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-the-exact-denoising-posterior","title":"Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models","date":"2025-06-16","arxiv_id":"2506.13614","repositories_listed":0,"syntology":null},{"url":null,"slug":"georecon-graph-level-representation-learning","title":"GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining","date":"2025-06-16","arxiv_id":"2506.13174","repositories_listed":0,"syntology":null},{"url":null,"slug":"limited-angle-cbct-reconstruction-via","title":"Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models","date":"2025-06-16","arxiv_id":"2506.13545","repositories_listed":0,"syntology":null},{"url":"/paper/sharpness-aware-machine-unlearning","slug":"sharpness-aware-machine-unlearning","title":"Sharpness-Aware Machine Unlearning","date":"2025-06-16","arxiv_id":"2506.13715","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/sharpness-aware-machine-unlearning#ran","syntology_url":"https://syntology.ai/paper/2506.13715","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.13715"}},"official":null}},{"url":null,"slug":"speechrefiner-towards-perceptual-quality","title":"SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms","date":"2025-06-16","arxiv_id":"2506.13709","repositories_listed":0,"syntology":null},{"url":null,"slug":"stage-a-stream-centric-generative-world-model","title":"STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation","date":"2025-06-16","arxiv_id":"2506.13138","repositories_listed":0,"syntology":null},{"url":null,"slug":"wisva-generative-ai-for-5g-network","title":"WISVA: Generative AI for 5G Network Optimization in Smart Warehouses","date":"2025-06-16","arxiv_id":"2506.22456","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-solving-of-imaging-inverse-problems","title":"Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models","date":"2025-06-16","arxiv_id":"2506.13391","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraint-guided-prediction-refinement-via","title":"Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories","date":"2025-06-15","arxiv_id":"2506.12911","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffs-nocs-3d-point-cloud-reconstruction","title":"DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models","date":"2025-06-15","arxiv_id":"2506.12835","repositories_listed":0,"syntology":null},{"url":null,"slug":"gm-ldm-latent-diffusion-model-for-brain","title":"GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis","date":"2025-06-15","arxiv_id":"2506.12719","repositories_listed":0,"syntology":null},{"url":null,"slug":"reframe-layer-caching-for-accelerated","title":"ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering","date":"2025-06-14","arxiv_id":"2506.13814","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffpr-diffusion-based-phase-reconstruction","title":"DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning","date":"2025-06-12","arxiv_id":"2506.11183","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-efficient-image-generation","title":"High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model","date":"2025-06-12","arxiv_id":"2506.10605","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-denoising-of-cryo-em-projection-images","title":"Joint Denoising of Cryo-EM Projection Images using Polar Transformers","date":"2025-06-12","arxiv_id":"2506.11283","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-transformers-with-insights-from","title":"Revisiting Transformers with Insights from Image Filtering","date":"2025-06-12","arxiv_id":"2506.10371","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10141","title":"Diffusion prior as a direct regularization term for FWI","date":"2025-06-11","arxiv_id":"2506.10141","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-generative-model-for-the-simulation-of","title":"A Deep Generative Model for the Simulation of Discrete Karst Networks","date":"2025-06-11","arxiv_id":"2506.09832","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-monte-carlo-tree-diffusion-100x-speedup","title":"Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning","date":"2025-06-11","arxiv_id":"2506.09498","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-aware-image-restoration-with-diffusion","title":"Text-Aware Image Restoration with Diffusion Models","date":"2025-06-11","arxiv_id":"2506.09993","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-unified-diffusion-policy-with-action","title":"Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation","date":"2025-06-11","arxiv_id":"2506.09422","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08459","title":"Diffusion Models for Safety Validation of Autonomous Driving Systems","date":"2025-06-10","arxiv_id":"2506.08459","repositories_listed":0,"syntology":null}],"record_sha256":"3f1d7d1d64db278277e27e4edba9a962e091db7a4841b618236e6d1dee004ccf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}