{"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/sensitivity/papers/2","list_of":"/task/sensitivity","task":"Sensitivity","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":2,"pages_in_order":21,"rows_per_page":100,"rows":[101,200],"of":2016,"counts":{"archive_papers_tagged":2016,"with_a_code_link":505,"where_syntology_ran_a_sample":100,"not_listed_spam_title":0,"listed":2016,"listed_where_code_ran":100,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":77,"every_run_a_failure_of_syntologys_instrument":23,"listed_with_a_run_with_no_instrument_failure":77,"listed_every_run_a_failure_of_syntologys_instrument":23,"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/sensitivity","prev":"/task/sensitivity","next":"/task/sensitivity/papers/3","papers":[{"url":"/paper/opengert-open-source-automated-geometry","slug":"opengert-open-source-automated-geometry","title":"OpenGERT: Open Source Automated Geometry Extraction with Geometric and Electromagnetic Sensitivity Analyses for Ray-Tracing Propagation Models","date":"2025-01-12","arxiv_id":"2501.06945","repositories_listed":1,"syntology":null},{"url":"/paper/radgpt-constructing-3d-image-text-tumor","slug":"radgpt-constructing-3d-image-text-tumor","title":"RadGPT: Constructing 3D Image-Text Tumor Datasets","date":"2025-01-08","arxiv_id":"2501.04678","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/radgpt-constructing-3d-image-text-tumor#ran","syntology_url":"https://syntology.ai/paper/2501.04678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.04678"}},"official":{"repos":["mrgiovanni/radgpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/who-does-the-giant-number-pile-like-best","slug":"who-does-the-giant-number-pile-like-best","title":"Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring Contexts","date":"2025-01-08","arxiv_id":"2501.04316","repositories_listed":1,"syntology":null},{"url":"/paper/embedding-style-beyond-topics-analyzing","slug":"embedding-style-beyond-topics-analyzing","title":"Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models","date":"2025-01-01","arxiv_id":"2501.00828","repositories_listed":1,"syntology":null},{"url":"/paper/t2icount-enhancing-cross-modal-understanding","slug":"t2icount-enhancing-cross-modal-understanding","title":"T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/expand-vsr-benchmark-for-vllm-to-expertize-in","slug":"expand-vsr-benchmark-for-vllm-to-expertize-in","title":"Expand VSR Benchmark for VLLM to Expertize in Spatial Rules","date":"2024-12-24","arxiv_id":"2412.18224","repositories_listed":1,"syntology":null},{"url":"/paper/meta-evaluating-stability-measures-max","slug":"meta-evaluating-stability-measures-max","title":"Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity","date":"2024-12-14","arxiv_id":"2412.10942","repositories_listed":1,"syntology":null},{"url":"/paper/loss-function-to-optimise-signal-significance","slug":"loss-function-to-optimise-signal-significance","title":"Loss function to optimise signal significance in particle physics","date":"2024-12-12","arxiv_id":"2412.09500","repositories_listed":1,"syntology":null},{"url":"/paper/physics-driven-autoregressive-state-space","slug":"physics-driven-autoregressive-state-space","title":"Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction","date":"2024-12-12","arxiv_id":"2412.09331","repositories_listed":1,"syntology":null},{"url":"/paper/a-method-for-evaluating-hyperparameter","slug":"a-method-for-evaluating-hyperparameter","title":"A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning","date":"2024-12-10","arxiv_id":"2412.07165","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-method-for-evaluating-hyperparameter#ran","syntology_url":"https://syntology.ai/paper/2412.07165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07165"}},"official":{"repos":["jadkins99/hyperparameter_sensitivity"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/asking-again-and-again-exploring-llm","slug":"asking-again-and-again-exploring-llm","title":"Asking Again and Again: Exploring LLM Robustness to Repeated Questions","date":"2024-12-10","arxiv_id":"2412.07923","repositories_listed":1,"syntology":null},{"url":"/paper/toward-non-invasive-diagnosis-of-bankart","slug":"toward-non-invasive-diagnosis-of-bankart","title":"Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning","date":"2024-12-09","arxiv_id":"2412.06717","repositories_listed":1,"syntology":null},{"url":"/paper/rl-for-mitigating-cascading-failures-targeted","slug":"rl-for-mitigating-cascading-failures-targeted","title":"RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors","date":"2024-11-27","arxiv_id":"2411.18050","repositories_listed":1,"syntology":null},{"url":"/paper/robustness-and-confounders-in-the-demographic","slug":"robustness-and-confounders-in-the-demographic","title":"Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness","date":"2024-11-13","arxiv_id":"2411.08977","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-context-sensitivity-and-the-knob","slug":"controllable-context-sensitivity-and-the-knob","title":"Controllable Context Sensitivity and the Knob Behind It","date":"2024-11-11","arxiv_id":"2411.07404","repositories_listed":1,"syntology":{"n":24,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":24,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/controllable-context-sensitivity-and-the-knob#ran","syntology_url":"https://syntology.ai/paper/2411.07404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07404"}},"official":{"repos":["kdu4108/context-vs-prior-finetuning"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/ajdmom-a-python-package-for-deriving-moment","slug":"ajdmom-a-python-package-for-deriving-moment","title":"ajdmom: A Python Package for Deriving Moment Formulas of Affine Jump Diffusion Processes","date":"2024-11-10","arxiv_id":"2411.06484","repositories_listed":1,"syntology":null},{"url":"/paper/da-moe-addressing-depth-sensitivity-in-graph","slug":"da-moe-addressing-depth-sensitivity-in-graph","title":"DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts","date":"2024-11-05","arxiv_id":"2411.03025","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/da-moe-addressing-depth-sensitivity-in-graph#ran","syntology_url":"https://syntology.ai/paper/2411.03025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.03025"}},"official":{"repos":["celin-yao/da-moe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/automated-assessment-of-residual-plots-with","slug":"automated-assessment-of-residual-plots-with","title":"Automated Assessment of Residual Plots with Computer Vision Models","date":"2024-11-01","arxiv_id":"2411.01001","repositories_listed":1,"syntology":null},{"url":"/paper/prototypical-hash-encoding-for-on-the-fly","slug":"prototypical-hash-encoding-for-on-the-fly","title":"Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery","date":"2024-10-24","arxiv_id":"2410.19213","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/prototypical-hash-encoding-for-on-the-fly#ran","syntology_url":"https://syntology.ai/paper/2410.19213","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.19213"}},"official":{"repos":["haiyangzheng/phe"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/causalgraph2llm-evaluating-llms-for-causal","slug":"causalgraph2llm-evaluating-llms-for-causal","title":"CausalGraph2LLM: Evaluating LLMs for Causal Queries","date":"2024-10-21","arxiv_id":"2410.15939","repositories_listed":1,"syntology":null},{"url":"/paper/prosa-assessing-and-understanding-the-prompt","slug":"prosa-assessing-and-understanding-the-prompt","title":"ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs","date":"2024-10-16","arxiv_id":"2410.12405","repositories_listed":1,"syntology":null},{"url":"/paper/navigating-the-cultural-kaleidoscope-a","slug":"navigating-the-cultural-kaleidoscope-a","title":"Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models","date":"2024-10-15","arxiv_id":"2410.12880","repositories_listed":1,"syntology":null},{"url":"/paper/rate-score-reward-models-with-imperfect","slug":"rate-score-reward-models-with-imperfect","title":"RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals","date":"2024-10-15","arxiv_id":"2410.11348","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rate-score-reward-models-with-imperfect#ran","syntology_url":"https://syntology.ai/paper/2410.11348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11348"}},"official":{"repos":["toddnief/rate"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cultural-heritage-3d-reconstruction-with","slug":"cultural-heritage-3d-reconstruction-with","title":"Cultural Heritage 3D Reconstruction with Diffusion Networks","date":"2024-10-14","arxiv_id":"2410.10927","repositories_listed":1,"syntology":null},{"url":"/paper/the-fragility-of-fairness-causal-sensitivity","slug":"the-fragility-of-fairness-causal-sensitivity","title":"The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning","date":"2024-10-12","arxiv_id":"2410.09600","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-fragility-of-fairness-causal-sensitivity#ran","syntology_url":"https://syntology.ai/paper/2410.09600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09600"}},"official":{"repos":["jakefawkes/fragile_fair"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-sparql-generation-by-triplet-order","slug":"enhancing-sparql-generation-by-triplet-order","title":"Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training","date":"2024-10-08","arxiv_id":"2410.05731","repositories_listed":1,"syntology":null},{"url":"/paper/give-me-a-hint-can-llms-take-a-hint-to-solve","slug":"give-me-a-hint-can-llms-take-a-hint-to-solve","title":"Give me a hint: Can LLMs take a hint to solve math problems?","date":"2024-10-08","arxiv_id":"2410.05915","repositories_listed":1,"syntology":null},{"url":"/paper/posix-a-prompt-sensitivity-index-for-large","slug":"posix-a-prompt-sensitivity-index-for-large","title":"POSIX: A Prompt Sensitivity Index For Large Language Models","date":"2024-10-03","arxiv_id":"2410.02185","repositories_listed":1,"syntology":null},{"url":"/paper/stop-benchmarking-large-language-models-with","slug":"stop-benchmarking-large-language-models-with","title":"STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions","date":"2024-09-20","arxiv_id":"2409.13843","repositories_listed":1,"syntology":null},{"url":"/paper/denomamba-a-fused-state-space-model-for-low","slug":"denomamba-a-fused-state-space-model-for-low","title":"DenoMamba: A fused state-space model for low-dose CT denoising","date":"2024-09-19","arxiv_id":"2409.13094","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-based-high-bandwidth","slug":"machine-learning-based-high-bandwidth","title":"Machine-learning based high-bandwidth magnetic sensing","date":"2024-09-19","arxiv_id":"2409.12820","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-pretrained-language-models-on","slug":"evaluation-of-pretrained-language-models-on","title":"Evaluation of pretrained language models on music understanding","date":"2024-09-17","arxiv_id":"2409.11449","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluation-of-pretrained-language-models-on#ran","syntology_url":"https://syntology.ai/paper/2409.11449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.11449"}},"official":{"repos":["YannisBilly/Evaluation-of-pretrained-language-models-on-music-understanding"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-bayesian-interpretation-of-adaptive-low","slug":"a-bayesian-interpretation-of-adaptive-low","title":"A Bayesian Interpretation of Adaptive Low-Rank Adaptation","date":"2024-09-16","arxiv_id":"2409.10673","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-bayesian-interpretation-of-adaptive-low#ran","syntology_url":"https://syntology.ai/paper/2409.10673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.10673"}},"official":{"repos":["idiap/vilora"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pushing-the-boundaries-of-event-subsampling","slug":"pushing-the-boundaries-of-event-subsampling","title":"Pushing the boundaries of event subsampling in event-based video classification using CNNs","date":"2024-09-13","arxiv_id":"2409.08953","repositories_listed":1,"syntology":null},{"url":"/paper/2409-13727","slug":"2409-13727","title":"Classification performance and reproducibility of GPT-4 omni for information extraction from veterinary electronic health records","date":"2024-09-09","arxiv_id":"2409.13727","repositories_listed":1,"syntology":null},{"url":"/paper/segmenting-object-affordances-reproducibility","slug":"segmenting-object-affordances-reproducibility","title":"Segmenting Object Affordances: Reproducibility and Sensitivity to Scale","date":"2024-09-03","arxiv_id":"2409.01814","repositories_listed":1,"syntology":null},{"url":"/paper/adapmoe-adaptive-sensitivity-based-expert","slug":"adapmoe-adaptive-sensitivity-based-expert","title":"AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference","date":"2024-08-19","arxiv_id":"2408.10284","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adapmoe-adaptive-sensitivity-based-expert#ran","syntology_url":"https://syntology.ai/paper/2408.10284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10284"}},"official":{"repos":["pku-sec-lab/adapmoe"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/impacts-of-floating-point-non-associativity","slug":"impacts-of-floating-point-non-associativity","title":"Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications","date":"2024-08-09","arxiv_id":"2408.05148","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/impacts-of-floating-point-non-associativity#ran","syntology_url":"https://syntology.ai/paper/2408.05148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.05148"}},"official":{"repos":["minnervva/correctness"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/combining-diverse-information-for-coordinated","slug":"combining-diverse-information-for-coordinated","title":"Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents","date":"2024-08-06","arxiv_id":"2408.03405","repositories_listed":1,"syntology":null},{"url":"/paper/a-large-scale-sensitivity-analysis-on-latent","slug":"a-large-scale-sensitivity-analysis-on-latent","title":"A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations","date":"2024-07-25","arxiv_id":"2407.17876","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervision-improves-diffusion-models","slug":"self-supervision-improves-diffusion-models","title":"Self-Supervision Improves Diffusion Models for Tabular Data Imputation","date":"2024-07-25","arxiv_id":"2407.18013","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervision-improves-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2407.18013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18013"}},"official":{"repos":["yixinliu233/simpdm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/enhancing-wrist-abnormality-detection-with","slug":"enhancing-wrist-abnormality-detection-with","title":"Enhancing Wrist Fracture Detection with YOLO","date":"2024-07-17","arxiv_id":"2407.12597","repositories_listed":1,"syntology":null},{"url":"/paper/a-theoretical-formulation-of-many-body","slug":"a-theoretical-formulation-of-many-body","title":"A Theoretical Formulation of Many-body Message Passing Neural Networks","date":"2024-07-16","arxiv_id":"2407.11756","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"1 ran (of which 0 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) · 3 unverified","sample_list":"/paper/a-theoretical-formulation-of-many-body#ran","syntology_url":"https://syntology.ai/paper/2407.11756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11756"}},"official":{"repos":["jthh/many-body-mpnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/busclean-open-source-software-for-breast","slug":"busclean-open-source-software-for-breast","title":"BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI","date":"2024-07-16","arxiv_id":"2407.11316","repositories_listed":1,"syntology":null},{"url":"/paper/active-learning-for-derivative-based-global","slug":"active-learning-for-derivative-based-global","title":"Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes","date":"2024-07-13","arxiv_id":"2407.09739","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-adversarial-robustness-of-2","slug":"evaluating-the-adversarial-robustness-of-2","title":"Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off","date":"2024-07-12","arxiv_id":"2407.09150","repositories_listed":1,"syntology":null},{"url":"/paper/chatgpt-doesn-t-trust-chargers-fans-guardrail","slug":"chatgpt-doesn-t-trust-chargers-fans-guardrail","title":"ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context","date":"2024-07-09","arxiv_id":"2407.06866","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/chatgpt-doesn-t-trust-chargers-fans-guardrail#ran","syntology_url":"https://syntology.ai/paper/2407.06866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06866"}},"official":{"repos":["vli31/llm-guardrail-sensitivity"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/dsmix-distortion-induced-sensitivity-map","slug":"dsmix-distortion-induced-sensitivity-map","title":"DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality Assessment","date":"2024-07-04","arxiv_id":"2407.03886","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-vertebral-fracture-analysis-with","slug":"explainable-vertebral-fracture-analysis-with","title":"Explainable vertebral fracture analysis with uncertainty estimation using differentiable rule-based classification","date":"2024-07-03","arxiv_id":"2407.02926","repositories_listed":1,"syntology":null},{"url":"/paper/social-bias-evaluation-for-large-language","slug":"social-bias-evaluation-for-large-language","title":"Social Bias Evaluation for Large Language Models Requires Prompt Variations","date":"2024-07-03","arxiv_id":"2407.03129","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/social-bias-evaluation-for-large-language#ran","syntology_url":"https://syntology.ai/paper/2407.03129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.03129"}},"official":{"repos":["rem-h4/llm_socialbias_prompts"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/close-but-not-there-boosting-geographic","slug":"close-but-not-there-boosting-geographic","title":"Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition","date":"2024-07-02","arxiv_id":"2407.02422","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"1 ran (of which 0 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) · 5 unverified","sample_list":"/paper/close-but-not-there-boosting-geographic#ran","syntology_url":"https://syntology.ai/paper/2407.02422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.02422"}},"official":{"repos":["serizba/cliquemining"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-bayesian-quadrature","slug":"conditional-bayesian-quadrature","title":"Conditional Bayesian Quadrature","date":"2024-06-24","arxiv_id":"2406.16530","repositories_listed":1,"syntology":null},{"url":"/paper/certificates-of-differential-privacy-and","slug":"certificates-of-differential-privacy-and","title":"Certification for Differentially Private Prediction in Gradient-Based Training","date":"2024-06-19","arxiv_id":"2406.13433","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/certificates-of-differential-privacy-and#ran","syntology_url":"https://syntology.ai/paper/2406.13433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13433"}},"official":{"repos":["psosnin/AbstractGradientTraining"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/coupled-input-output-dimension-reduction","slug":"coupled-input-output-dimension-reduction","title":"Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis","date":"2024-06-19","arxiv_id":"2406.13425","repositories_listed":1,"syntology":null},{"url":"/paper/what-did-i-do-wrong-quantifying-llms","slug":"what-did-i-do-wrong-quantifying-llms","title":"What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering","date":"2024-06-18","arxiv_id":"2406.12334","repositories_listed":1,"syntology":null},{"url":"/paper/sugarcrepe-dataset-vision-language-model","slug":"sugarcrepe-dataset-vision-language-model","title":"SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations","date":"2024-06-17","arxiv_id":"2406.11171","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sugarcrepe-dataset-vision-language-model#ran","syntology_url":"https://syntology.ai/paper/2406.11171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11171"}},"official":{"repos":["Sri-Harsha/scpp"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/expected-grad-cam-towards-gradient","slug":"expected-grad-cam-towards-gradient","title":"Expected Grad-CAM: Towards gradient faithfulness","date":"2024-06-03","arxiv_id":"2406.01274","repositories_listed":1,"syntology":null},{"url":"/paper/sam-vmnet-deep-neural-networks-for-coronary","slug":"sam-vmnet-deep-neural-networks-for-coronary","title":"A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images","date":"2024-06-01","arxiv_id":"2406.00492","repositories_listed":1,"syntology":null},{"url":"/paper/constraining-the-higgs-potential-with-neural","slug":"constraining-the-higgs-potential-with-neural","title":"Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production","date":"2024-05-24","arxiv_id":"2405.15847","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/constraining-the-higgs-potential-with-neural#ran","syntology_url":"https://syntology.ai/paper/2405.15847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.15847"}},"official":{"repos":["rmastand/nsbi_for_dihiggs"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ferrari-federated-feature-unlearning-via","slug":"ferrari-federated-feature-unlearning-via","title":"Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity","date":"2024-05-23","arxiv_id":"2405.17462","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ferrari-federated-feature-unlearning-via#ran","syntology_url":"https://syntology.ai/paper/2405.17462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17462"}},"official":{"repos":["ongwinkent/federated-feature-unlearning"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/i2i-mamba-multi-modal-medical-image-synthesis","slug":"i2i-mamba-multi-modal-medical-image-synthesis","title":"I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling","date":"2024-05-22","arxiv_id":"2405.14022","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-exoplanet-detection-in","slug":"machine-learning-for-exoplanet-detection-in","title":"Machine learning for exoplanet detection in high-contrast spectroscopy Combining cross correlation maps and deep learning on medium-resolution integral-field spectra","date":"2024-05-22","arxiv_id":"2405.13468","repositories_listed":1,"syntology":null},{"url":"/paper/multicenter-privacy-preserving-model-training","slug":"multicenter-privacy-preserving-model-training","title":"Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation","date":"2024-05-17","arxiv_id":"2405.10870","repositories_listed":1,"syntology":null},{"url":"/paper/generalization-bounds-for-causal-regression","slug":"generalization-bounds-for-causal-regression","title":"Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis","date":"2024-05-15","arxiv_id":"2405.09516","repositories_listed":1,"syntology":null},{"url":"/paper/drgat-attention-guided-gene-assessment-of","slug":"drgat-attention-guided-gene-assessment-of","title":"drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network","date":"2024-05-14","arxiv_id":"2405.08979","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-pseudo-nearest-neighbor","slug":"a-novel-pseudo-nearest-neighbor","title":"A Novel Pseudo Nearest Neighbor Classification Method Using Local Harmonic Mean Distance","date":"2024-05-10","arxiv_id":"2405.06238","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-chatgpt-for-diagnosing-autism","slug":"exploiting-chatgpt-for-diagnosing-autism","title":"Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features","date":"2024-05-03","arxiv_id":"2405.01799","repositories_listed":1,"syntology":null},{"url":"/paper/inherent-trade-offs-between-diversity-and","slug":"inherent-trade-offs-between-diversity-and","title":"Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks","date":"2024-05-02","arxiv_id":"2405.01719","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/inherent-trade-offs-between-diversity-and#ran","syntology_url":"https://syntology.ai/paper/2405.01719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01719"}},"official":{"repos":["socialfoundations/benchbench"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/using-deep-learning-to-identify-initial-error","slug":"using-deep-learning-to-identify-initial-error","title":"Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts","date":"2024-04-23","arxiv_id":"2404.15419","repositories_listed":1,"syntology":null},{"url":"/paper/piercing-the-veil-of-tvl-defi-reappraised","slug":"piercing-the-veil-of-tvl-defi-reappraised","title":"Piercing the Veil of TVL: DeFi Reappraised","date":"2024-04-17","arxiv_id":"2404.11745","repositories_listed":1,"syntology":null},{"url":"/paper/a-provable-control-of-sensitivity-of-neural","slug":"a-provable-control-of-sensitivity-of-neural","title":"A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness","date":"2024-04-15","arxiv_id":"2404.09821","repositories_listed":1,"syntology":null},{"url":"/paper/transformers-contextualism-and-polysemy","slug":"transformers-contextualism-and-polysemy","title":"Transformers, Contextualism, and Polysemy","date":"2024-04-15","arxiv_id":"2404.09577","repositories_listed":1,"syntology":null},{"url":"/paper/pasa-attack-agnostic-unsupervised-adversarial","slug":"pasa-attack-agnostic-unsupervised-adversarial","title":"PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis","date":"2024-04-12","arxiv_id":"2404.10789","repositories_listed":1,"syntology":null},{"url":"/paper/legrad-an-explainability-method-for-vision","slug":"legrad-an-explainability-method-for-vision","title":"LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity","date":"2024-04-04","arxiv_id":"2404.03214","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":0,"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/legrad-an-explainability-method-for-vision#ran","syntology_url":"https://syntology.ai/paper/2404.03214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03214"}},"official":{"repos":["walbouss/legrad"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/settling-time-vs-accuracy-tradeoffs-for","slug":"settling-time-vs-accuracy-tradeoffs-for","title":"Settling Time vs. Accuracy Tradeoffs for Clustering Big Data","date":"2024-04-02","arxiv_id":"2404.01936","repositories_listed":1,"syntology":null},{"url":"/paper/variational-design-of-sensory-feedback-for","slug":"variational-design-of-sensory-feedback-for","title":"Variational design of sensory feedback for powerstroke-recovery systems","date":"2024-03-29","arxiv_id":"2404.00111","repositories_listed":1,"syntology":null},{"url":"/paper/infrared-small-target-detection-with-scale","slug":"infrared-small-target-detection-with-scale","title":"Infrared Small Target Detection with Scale and Location Sensitivity","date":"2024-03-28","arxiv_id":"2403.19366","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/infrared-small-target-detection-with-scale#ran","syntology_url":"https://syntology.ai/paper/2403.19366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19366"}},"official":{"repos":["ying-fu/mshnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-pre-trained-language-model","slug":"improving-pre-trained-language-model","title":"Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER","date":"2024-03-26","arxiv_id":"2403.18025","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-risk-of-cardiovascular-disease","slug":"predicting-risk-of-cardiovascular-disease","title":"Predicting risk of cardiovascular disease using retinal OCT imaging","date":"2024-03-26","arxiv_id":"2403.18873","repositories_listed":1,"syntology":null},{"url":"/paper/equipping-computational-pathology-systems","slug":"equipping-computational-pathology-systems","title":"Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs","date":"2024-03-12","arxiv_id":"2403.07743","repositories_listed":1,"syntology":null},{"url":"/paper/validation-of-ml-uq-calibration-statistics","slug":"validation-of-ml-uq-calibration-statistics","title":"Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis","date":"2024-03-01","arxiv_id":"2403.00423","repositories_listed":1,"syntology":null},{"url":"/paper/variational-learning-is-effective-for-large","slug":"variational-learning-is-effective-for-large","title":"Variational Learning is Effective for Large Deep Networks","date":"2024-02-27","arxiv_id":"2402.17641","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/variational-learning-is-effective-for-large#ran","syntology_url":"https://syntology.ai/paper/2402.17641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17641"}},"official":{"repos":["team-approx-bayes/ivon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multicontrievers-analysis-of-dense-retrieval","slug":"multicontrievers-analysis-of-dense-retrieval","title":"MultiContrievers: Analysis of Dense Retrieval Representations","date":"2024-02-24","arxiv_id":"2402.15925","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-order-sensitivity-of-in-context","slug":"addressing-order-sensitivity-of-in-context","title":"Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models","date":"2024-02-23","arxiv_id":"2402.15637","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/addressing-order-sensitivity-of-in-context#ran","syntology_url":"https://syntology.ai/paper/2402.15637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15637"}},"official":{"repos":["xyzcs/infoac"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-sensitivity-of-learning-with-limited","slug":"on-sensitivity-of-learning-with-limited","title":"On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices","date":"2024-02-20","arxiv_id":"2402.12817","repositories_listed":1,"syntology":null},{"url":"/paper/why-are-sensitive-functions-hard-for","slug":"why-are-sensitive-functions-hard-for","title":"Why are Sensitive Functions Hard for Transformers?","date":"2024-02-15","arxiv_id":"2402.09963","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/why-are-sensitive-functions-hard-for#ran","syntology_url":"https://syntology.ai/paper/2402.09963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09963"}},"official":{"repos":["lacoco-lab/sensitivity-hardness"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-with-diversification-from-block","slug":"learning-with-diversification-from-block","title":"Block Sparse Bayesian Learning: A Diversified Scheme","date":"2024-02-07","arxiv_id":"2402.04646","repositories_listed":1,"syntology":null},{"url":"/paper/pard-permutation-invariant-autoregressive","slug":"pard-permutation-invariant-autoregressive","title":"Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation","date":"2024-02-06","arxiv_id":"2402.03687","repositories_listed":1,"syntology":null},{"url":"/paper/towards-understanding-the-word-sensitivity-of","slug":"towards-understanding-the-word-sensitivity-of","title":"Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features","date":"2024-02-05","arxiv_id":"2402.02969","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-understanding-the-word-sensitivity-of#ran","syntology_url":"https://syntology.ai/paper/2402.02969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02969"}},"official":{"repos":["simone-bombari/attention-sensitivity"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/when-benchmarks-are-targets-revealing-the","slug":"when-benchmarks-are-targets-revealing-the","title":"When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards","date":"2024-02-01","arxiv_id":"2402.01781","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/when-benchmarks-are-targets-revealing-the#ran","syntology_url":"https://syntology.ai/paper/2402.01781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01781"}},"official":{"repos":["national-center-for-ai-saudi-arabia/lm-evaluation-harness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interpreting-time-series-transformer-models","slug":"interpreting-time-series-transformer-models","title":"Interpreting Time Series Transformer Models and Sensitivity Analysis of Population Age Groups to COVID-19 Infections","date":"2024-01-26","arxiv_id":"2401.15119","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-sensitivities-and-inconsistent","slug":"semantic-sensitivities-and-inconsistent","title":"Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models","date":"2024-01-25","arxiv_id":"2401.14440","repositories_listed":1,"syntology":null},{"url":"/paper/development-of-rlk-unet-a-clinically","slug":"development-of-rlk-unet-a-clinically","title":"Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment","date":"2024-01-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scissorhands-scrub-data-influence-via","slug":"scissorhands-scrub-data-influence-via","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","date":"2024-01-11","arxiv_id":"2401.06187","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/scissorhands-scrub-data-influence-via#ran","syntology_url":"https://syntology.ai/paper/2401.06187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06187"}},"official":{"repos":["jingwu321/scissorhands"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/exploring-the-sensitivity-of-llms-decision","slug":"exploring-the-sensitivity-of-llms-decision","title":"Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters","date":"2023-12-29","arxiv_id":"2312.17476","repositories_listed":1,"syntology":null},{"url":"/paper/retailsynth-synthetic-data-generation-for","slug":"retailsynth-synthetic-data-generation-for","title":"RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation","date":"2023-12-21","arxiv_id":"2312.14095","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-the-urysohn-lemma-of-topology-for","slug":"leveraging-the-urysohn-lemma-of-topology-for","title":"Leveraging the Urysohn Lemma of Topology for an Enhanced Binary Classifier","date":"2023-12-19","arxiv_id":"2312.11948","repositories_listed":1,"syntology":null},{"url":"/paper/the-curious-case-of-the-test-set-auroc","slug":"the-curious-case-of-the-test-set-auroc","title":"The curious case of the test set AUROC","date":"2023-12-19","arxiv_id":"2312.16188","repositories_listed":1,"syntology":null},{"url":"/paper/asvd-activation-aware-singular-value","slug":"asvd-activation-aware-singular-value","title":"ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models","date":"2023-12-10","arxiv_id":"2312.05821","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/asvd-activation-aware-singular-value#ran","syntology_url":"https://syntology.ai/paper/2312.05821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.05821"}},"official":{"repos":["hahnyuan/asvd4llm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/surface-coil-intensity-correction-for-mri","slug":"surface-coil-intensity-correction-for-mri","title":"Surface Coil Intensity Correction for MRI","date":"2023-12-01","arxiv_id":"2312.00936","repositories_listed":1,"syntology":null}],"record_sha256":"e42f62bb717415f90062579412bb9357b48b91f4d02480c50860a1c7e21987ae","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}