{"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/decision-making/papers/21","list_of":"/task/decision-making","task":"Decision Making","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":21,"pages_in_order":124,"rows_per_page":100,"rows":[2001,2100],"of":12311,"counts":{"archive_papers_tagged":12311,"with_a_code_link":2946,"where_syntology_ran_a_sample":678,"not_listed_spam_title":0,"listed":12311,"listed_where_code_ran":678,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":560,"every_run_a_failure_of_syntologys_instrument":118,"listed_with_a_run_with_no_instrument_failure":560,"listed_every_run_a_failure_of_syntologys_instrument":118,"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/decision-making","prev":"/task/decision-making/papers/20","next":"/task/decision-making/papers/22","papers":[{"url":"/paper/qagcn-a-graph-convolutional-network-based","slug":"qagcn-a-graph-convolutional-network-based","title":"QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs","date":"2022-06-03","arxiv_id":"2206.01818","repositories_listed":1,"syntology":null},{"url":"/paper/rashomon-capacity-a-metric-for-predictive","slug":"rashomon-capacity-a-metric-for-predictive","title":"Rashomon Capacity: A Metric for Predictive Multiplicity in Classification","date":"2022-06-02","arxiv_id":"2206.01295","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/rashomon-capacity-a-metric-for-predictive#ran","syntology_url":"https://syntology.ai/paper/2206.01295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01295"}},"official":{"repos":["hsianghsu/rashomon-capacity"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-recurrence-modeling-for-video-action-1","slug":"unified-recurrence-modeling-for-video-action-1","title":"Unified Recurrence Modeling for Video Action Anticipation","date":"2022-06-02","arxiv_id":"2206.01009","repositories_listed":1,"syntology":null},{"url":"/paper/dynaformer-a-deep-learning-model-for-ageing","slug":"dynaformer-a-deep-learning-model-for-ageing","title":"Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction","date":"2022-06-01","arxiv_id":"2206.02555","repositories_listed":1,"syntology":null},{"url":"/paper/how-biased-is-your-feature-computing-fairness","slug":"how-biased-is-your-feature-computing-fairness","title":"How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis","date":"2022-06-01","arxiv_id":"2206.00667","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-up-discourse-quality-annotation-for","slug":"scaling-up-discourse-quality-annotation-for","title":"Scaling up Discourse Quality Annotation for Political Science","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-meta-reinforcement-learning-approach-for","slug":"a-meta-reinforcement-learning-approach-for","title":"A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud","date":"2022-05-31","arxiv_id":"2205.15795","repositories_listed":1,"syntology":null},{"url":"/paper/concept-level-debugging-of-part-prototype","slug":"concept-level-debugging-of-part-prototype","title":"Concept-level Debugging of Part-Prototype Networks","date":"2022-05-31","arxiv_id":"2205.15769","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/concept-level-debugging-of-part-prototype#ran","syntology_url":"https://syntology.ai/paper/2205.15769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15769"}},"official":{"repos":["abonte/protopdebug"],"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/robust-anytime-learning-of-markov-decision","slug":"robust-anytime-learning-of-markov-decision","title":"Robust Anytime Learning of Markov Decision Processes","date":"2022-05-31","arxiv_id":"2205.15827","repositories_listed":1,"syntology":null},{"url":"/paper/flicu-a-federated-learning-workflow-for","slug":"flicu-a-federated-learning-workflow-for","title":"FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction","date":"2022-05-30","arxiv_id":"2205.15104","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-reinforcement-learning-is-a","slug":"multi-agent-reinforcement-learning-is-a","title":"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem","date":"2022-05-30","arxiv_id":"2205.14953","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-treatment-effects-estimation","slug":"heterogeneous-treatment-effects-estimation","title":"Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects","date":"2022-05-29","arxiv_id":"2205.14714","repositories_listed":1,"syntology":null},{"url":"/paper/unfooling-perturbation-based-post-hoc","slug":"unfooling-perturbation-based-post-hoc","title":"Unfooling Perturbation-Based Post Hoc Explainers","date":"2022-05-29","arxiv_id":"2205.14772","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unfooling-perturbation-based-post-hoc#ran","syntology_url":"https://syntology.ai/paper/2205.14772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14772"}},"official":{"repos":["craymichael/unfooling"],"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"]}}},{"url":"/paper/a-confidence-machine-for-sparse-high-order","slug":"a-confidence-machine-for-sparse-high-order","title":"A Confidence Machine for Sparse High-Order Interaction Model","date":"2022-05-28","arxiv_id":"2205.14317","repositories_listed":1,"syntology":null},{"url":"/paper/functional-linear-regression-of-cdfs","slug":"functional-linear-regression-of-cdfs","title":"Functional Linear Regression of Cumulative Distribution Functions","date":"2022-05-28","arxiv_id":"2205.14545","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/functional-linear-regression-of-cdfs#ran","syntology_url":"https://syntology.ai/paper/2205.14545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14545"}},"official":{"repos":["qianzhang20/functional-linear-regression-of-cdfs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/alma-hierarchical-learning-for-composite","slug":"alma-hierarchical-learning-for-composite","title":"ALMA: Hierarchical Learning for Composite Multi-Agent Tasks","date":"2022-05-27","arxiv_id":"2205.14205","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/alma-hierarchical-learning-for-composite#ran","syntology_url":"https://syntology.ai/paper/2205.14205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14205"}},"official":{"repos":["shariqiqbal2810/alma"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/failure-detection-in-medical-image","slug":"failure-detection-in-medical-image","title":"Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed","date":"2022-05-27","arxiv_id":"2205.14094","repositories_listed":1,"syntology":null},{"url":"/paper/gispy-a-tool-for-measuring-gist-inference","slug":"gispy-a-tool-for-measuring-gist-inference","title":"GisPy: A Tool for Measuring Gist Inference Score in Text","date":"2022-05-25","arxiv_id":"2205.12484","repositories_listed":1,"syntology":null},{"url":"/paper/inductive-learning-of-complex-knowledge-from","slug":"inductive-learning-of-complex-knowledge-from","title":"Neuro-Symbolic Learning of Answer Set Programs from Raw Data","date":"2022-05-25","arxiv_id":"2205.12735","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-deviation-types-and-learning-for-1","slug":"efficient-deviation-types-and-learning-for-1","title":"Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections","date":"2022-05-24","arxiv_id":"2205.12031","repositories_listed":1,"syntology":null},{"url":"/paper/a-domain-adaptive-pre-training-approach-for","slug":"a-domain-adaptive-pre-training-approach-for","title":"A Domain-adaptive Pre-training Approach for Language Bias Detection in News","date":"2022-05-22","arxiv_id":"2205.10773","repositories_listed":1,"syntology":null},{"url":"/paper/a-review-of-safe-reinforcement-learning","slug":"a-review-of-safe-reinforcement-learning","title":"A Review of Safe Reinforcement Learning: Methods, Theory and Applications","date":"2022-05-20","arxiv_id":"2205.10330","repositories_listed":1,"syntology":null},{"url":"/paper/towards-explanation-for-unsupervised-graph","slug":"towards-explanation-for-unsupervised-graph","title":"Towards Explanation for Unsupervised Graph-Level Representation Learning","date":"2022-05-20","arxiv_id":"2205.09934","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-greedy-search-tracking-by-multi-agent","slug":"beyond-greedy-search-tracking-by-multi-agent","title":"Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search","date":"2022-05-19","arxiv_id":"2205.09676","repositories_listed":1,"syntology":null},{"url":"/paper/one-explanation-to-rule-them-all-ensemble","slug":"one-explanation-to-rule-them-all-ensemble","title":"One Explanation to Rule them All -- Ensemble Consistent Explanations","date":"2022-05-18","arxiv_id":"2205.08974","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-local-explanations-of-reject","slug":"model-agnostic-local-explanations-of-reject","title":"Model Agnostic Local Explanations of Reject","date":"2022-05-16","arxiv_id":"2205.07623","repositories_listed":1,"syntology":null},{"url":"/paper/goalnet-inferring-conjunctive-goal-predicates","slug":"goalnet-inferring-conjunctive-goal-predicates","title":"GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction Following","date":"2022-05-14","arxiv_id":"2205.07081","repositories_listed":1,"syntology":null},{"url":"/paper/sibila-high-performance-computing-and","slug":"sibila-high-performance-computing-and","title":"SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts","date":"2022-05-12","arxiv_id":"2205.06234","repositories_listed":1,"syntology":null},{"url":"/paper/de-biasing-bias-measurement","slug":"de-biasing-bias-measurement","title":"De-biasing \"bias\" measurement","date":"2022-05-11","arxiv_id":"2205.05770","repositories_listed":1,"syntology":null},{"url":"/paper/don-t-throw-it-away-the-utility-of-unlabeled","slug":"don-t-throw-it-away-the-utility-of-unlabeled","title":"Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making","date":"2022-05-10","arxiv_id":"2205.04790","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/don-t-throw-it-away-the-utility-of-unlabeled#ran","syntology_url":"https://syntology.ai/paper/2205.04790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.04790"}},"official":{"repos":["ayanmaj92/fairall"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/affective-medical-estimation-and-decision","slug":"affective-medical-estimation-and-decision","title":"Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning","date":"2022-05-09","arxiv_id":"2205.04599","repositories_listed":1,"syntology":null},{"url":"/paper/let-s-go-to-the-alien-zoo-introducing-an","slug":"let-s-go-to-the-alien-zoo-introducing-an","title":"Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning","date":"2022-05-06","arxiv_id":"2205.03398","repositories_listed":1,"syntology":null},{"url":"/paper/variance-reduction-based-partial-trajectory","slug":"variance-reduction-based-partial-trajectory","title":"Variance Reduction based Partial Trajectory Reuse to Accelerate Policy Gradient Optimization","date":"2022-05-06","arxiv_id":"2205.02976","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-knowledge-graph-embedding","slug":"explainable-knowledge-graph-embedding","title":"Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions","date":"2022-05-04","arxiv_id":"2205.01836","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-symmetry-for-improved","slug":"probabilistic-symmetry-for-improved","title":"Probabilistic Symmetry for Multi-Agent Dynamics","date":"2022-05-04","arxiv_id":"2205.01927","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/probabilistic-symmetry-for-improved#ran","syntology_url":"https://syntology.ai/paper/2205.01927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01927"}},"official":{"repos":["rose-stl-lab/pecco"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/evaluating-deep-taylor-decomposition-for","slug":"evaluating-deep-taylor-decomposition-for","title":"Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild","date":"2022-05-03","arxiv_id":"2206.02661","repositories_listed":1,"syntology":null},{"url":"/paper/conversational-ai-for-positive-sum-retailing","slug":"conversational-ai-for-positive-sum-retailing","title":"Conversational AI for Positive-sum Retailing under Falsehood Control","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dddm-a-brain-inspired-framework-for-robust","slug":"dddm-a-brain-inspired-framework-for-robust","title":"DDDM: a Brain-Inspired Framework for Robust Classification","date":"2022-05-01","arxiv_id":"2205.10117","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-multivariate-time","slug":"differentially-private-multivariate-time","title":"Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?","date":"2022-05-01","arxiv_id":"2205.00436","repositories_listed":1,"syntology":null},{"url":"/paper/reinforced-cross-modal-alignment-for","slug":"reinforced-cross-modal-alignment-for","title":"Reinforced Cross-modal Alignment for Radiology Report Generation","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adapting-and-evaluating-influence-estimation","slug":"adapting-and-evaluating-influence-estimation","title":"Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees","date":"2022-04-30","arxiv_id":"2205.00359","repositories_listed":1,"syntology":null},{"url":"/paper/engineering-flexible-machine-learning-systems","slug":"engineering-flexible-machine-learning-systems","title":"Engineering flexible machine learning systems by traversing functionally-invariant paths","date":"2022-04-30","arxiv_id":"2205.00334","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-artificial-intelligence-for-6","slug":"explainable-artificial-intelligence-for-6","title":"Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics","date":"2022-04-30","arxiv_id":"2205.00202","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"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 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) · 0 unverified","sample_list":"/paper/explainable-artificial-intelligence-for-6#ran","syntology_url":"https://syntology.ai/paper/2205.00202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00202"}},"official":{"repos":["maikejulie/DNN4Cli"],"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"]}}},{"url":"/paper/open-challenges-for-machine-learning-based","slug":"open-challenges-for-machine-learning-based","title":"Open challenges for Machine Learning based Early Decision-Making research","date":"2022-04-27","arxiv_id":"2204.13111","repositories_listed":1,"syntology":null},{"url":"/paper/evolutionary-multi-armed-bandits-with-genetic","slug":"evolutionary-multi-armed-bandits-with-genetic","title":"Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling","date":"2022-04-26","arxiv_id":"2205.10113","repositories_listed":1,"syntology":null},{"url":"/paper/toward-policy-explanations-for-multi-agent","slug":"toward-policy-explanations-for-multi-agent","title":"Toward Policy Explanations for Multi-Agent Reinforcement Learning","date":"2022-04-26","arxiv_id":"2204.12568","repositories_listed":1,"syntology":null},{"url":"/paper/on-machine-learning-driven-surrogates-for","slug":"on-machine-learning-driven-surrogates-for","title":"On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations","date":"2022-04-25","arxiv_id":"2204.12290","repositories_listed":1,"syntology":null},{"url":"/paper/covid-net-biochem-an-explainability-driven","slug":"covid-net-biochem-an-explainability-driven","title":"COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data","date":"2022-04-24","arxiv_id":"2204.11210","repositories_listed":1,"syntology":null},{"url":"/paper/6gan-ipv6-multi-pattern-target-generation-via","slug":"6gan-ipv6-multi-pattern-target-generation-via","title":"6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning","date":"2022-04-21","arxiv_id":"2204.09839","repositories_listed":1,"syntology":null},{"url":"/paper/from-point-forecasts-to-multivariate","slug":"from-point-forecasts-to-multivariate","title":"From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting","date":"2022-04-21","arxiv_id":"2204.10154","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-a-two-echelon","slug":"deep-reinforcement-learning-for-a-two-echelon","title":"Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains","date":"2022-04-20","arxiv_id":"2204.09603","repositories_listed":1,"syntology":null},{"url":"/paper/neurochaos-feature-transformation-and","slug":"neurochaos-feature-transformation-and","title":"Neurochaos Feature Transformation and Classification for Imbalanced Learning","date":"2022-04-20","arxiv_id":"2205.06742","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-hypergraph-self-supervised","slug":"spatial-temporal-hypergraph-self-supervised","title":"Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction","date":"2022-04-18","arxiv_id":"2204.08587","repositories_listed":1,"syntology":null},{"url":"/paper/lefm-nets-learnable-explicit-feature-map-deep","slug":"lefm-nets-learnable-explicit-feature-map-deep","title":"LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections","date":"2022-04-14","arxiv_id":"2204.06955","repositories_listed":1,"syntology":null},{"url":"/paper/occam-s-laser-occlusion-based-attribution","slug":"occam-s-laser-occlusion-based-attribution","title":"OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data","date":"2022-04-13","arxiv_id":"2204.06577","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":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) · 2 unverified","sample_list":"/paper/occam-s-laser-occlusion-based-attribution#ran","syntology_url":"https://syntology.ai/paper/2204.06577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06577"}},"official":{"repos":["dschinagl/occam"],"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/optimal-intermittent-particle-filter","slug":"optimal-intermittent-particle-filter","title":"Optimal Intermittent Particle Filter","date":"2022-04-13","arxiv_id":"2204.06265","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-reaction-aware-substructures-for","slug":"leveraging-reaction-aware-substructures-for","title":"Leveraging Reaction-aware Substructures for Retrosynthesis Analysis","date":"2022-04-12","arxiv_id":"2204.05919","repositories_listed":1,"syntology":null},{"url":"/paper/hft-lifting-perspective-representations-via","slug":"hft-lifting-perspective-representations-via","title":"HFT: Lifting Perspective Representations via Hybrid Feature Transformation","date":"2022-04-11","arxiv_id":"2204.05068","repositories_listed":1,"syntology":null},{"url":"/paper/so2sat-pop-a-curated-benchmark-data-set-for","slug":"so2sat-pop-a-curated-benchmark-data-set-for","title":"So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale","date":"2022-04-07","arxiv_id":"2204.08524","repositories_listed":1,"syntology":null},{"url":"/paper/pandr-fast-adaptation-to-new-environments","slug":"pandr-fast-adaptation-to-new-environments","title":"PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations","date":"2022-04-06","arxiv_id":"2204.02877","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":2,"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/pandr-fast-adaptation-to-new-environments#ran","syntology_url":"https://syntology.ai/paper/2204.02877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02877"}},"official":null}},{"url":"/paper/thinking-inside-the-box-learning-hypercube","slug":"thinking-inside-the-box-learning-hypercube","title":"Thinking inside The Box: Learning Hypercube Representations for Group Recommendation","date":"2022-04-06","arxiv_id":"2204.02592","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-rankings-into-quantized-scores-in","slug":"integrating-rankings-into-quantized-scores-in","title":"Integrating Rankings into Quantized Scores in Peer Review","date":"2022-04-05","arxiv_id":"2204.03505","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/integrating-rankings-into-quantized-scores-in#ran","syntology_url":"https://syntology.ai/paper/2204.03505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03505"}},"official":{"repos":["myusha/rankings_and_quantized_scores"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-agent-distributed-reinforcement","slug":"multi-agent-distributed-reinforcement","title":"Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions","date":"2022-04-05","arxiv_id":"2204.02267","repositories_listed":1,"syntology":null},{"url":"/paper/achieving-long-term-fairness-in-sequential","slug":"achieving-long-term-fairness-in-sequential","title":"Achieving Long-Term Fairness in Sequential Decision Making","date":"2022-04-04","arxiv_id":"2204.01819","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":1,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/achieving-long-term-fairness-in-sequential#ran","syntology_url":"https://syntology.ai/paper/2204.01819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01819"}},"official":{"repos":["yaoweihu/achieving-long-term-fairness"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-reinforcement-learning-approach-to-sensing","slug":"a-reinforcement-learning-approach-to-sensing","title":"A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems","date":"2022-04-01","arxiv_id":"2204.00703","repositories_listed":1,"syntology":null},{"url":"/paper/an-interpretable-probabilistic-autoregressive","slug":"an-interpretable-probabilistic-autoregressive","title":"Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting","date":"2022-04-01","arxiv_id":"2204.09640","repositories_listed":1,"syntology":null},{"url":"/paper/gazing-at-social-interactions-between","slug":"gazing-at-social-interactions-between","title":"Gazing at Social Interactions Between Foraging and Decision Theory","date":"2022-03-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scientometric-review-of-artificial","slug":"scientometric-review-of-artificial","title":"Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future","date":"2022-03-30","arxiv_id":"2204.02360","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-temporal","slug":"unsupervised-learning-of-temporal","title":"Unsupervised Learning of Temporal Abstractions with Slot-based Transformers","date":"2022-03-25","arxiv_id":"2203.13573","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unsupervised-learning-of-temporal#ran","syntology_url":"https://syntology.ai/paper/2203.13573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13573"}},"official":{"repos":["agopal42/slottar"],"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/multi-armed-bandits-for-online-optimization","slug":"multi-armed-bandits-for-online-optimization","title":"Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking","date":"2022-03-24","arxiv_id":"2203.13151","repositories_listed":1,"syntology":null},{"url":"/paper/hop-history-and-order-aware-pre-training-for","slug":"hop-history-and-order-aware-pre-training-for","title":"HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation","date":"2022-03-22","arxiv_id":"2203.11591","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hop-history-and-order-aware-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2203.11591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11591"}},"official":{"repos":["yanyuanqiao/hop-vln"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sweinet-deep-learning-based-uncertainty","slug":"sweinet-deep-learning-based-uncertainty","title":"SweiNet: Deep Learning Based Uncertainty Quantification for Ultrasound Shear Wave Elasticity Imaging","date":"2022-03-21","arxiv_id":"2203.10678","repositories_listed":1,"syntology":null},{"url":"/paper/teachable-reinforcement-learning-via-advice-1","slug":"teachable-reinforcement-learning-via-advice-1","title":"Teachable Reinforcement Learning via Advice Distillation","date":"2022-03-19","arxiv_id":"2203.11197","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":0,"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/teachable-reinforcement-learning-via-advice-1#ran","syntology_url":"https://syntology.ai/paper/2203.11197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11197"}},"official":{"repos":["rll-research/teachable"],"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/the-sandbox-environment-for-generalizable","slug":"the-sandbox-environment-for-generalizable","title":"The Sandbox Environment for Generalizable Agent Research (SEGAR)","date":"2022-03-19","arxiv_id":"2203.10351","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-flow-nerf-accurate-3d-modelling","slug":"conditional-flow-nerf-accurate-3d-modelling","title":"Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification","date":"2022-03-18","arxiv_id":"2203.10192","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/conditional-flow-nerf-accurate-3d-modelling#ran","syntology_url":"https://syntology.ai/paper/2203.10192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10192"}},"official":{"repos":["poetrywanderer/CF-NeRF"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/distraction-is-all-you-need-for-fairness","slug":"distraction-is-all-you-need-for-fairness","title":"Distraction is All You Need for Fairness","date":"2022-03-15","arxiv_id":"2203.07593","repositories_listed":1,"syntology":null},{"url":"/paper/socialvae-human-trajectory-prediction-using","slug":"socialvae-human-trajectory-prediction-using","title":"SocialVAE: Human Trajectory Prediction using Timewise Latents","date":"2022-03-15","arxiv_id":"2203.08207","repositories_listed":1,"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/socialvae-human-trajectory-prediction-using#ran","syntology_url":"https://syntology.ai/paper/2203.08207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08207"}},"official":{"repos":["xupei0610/socialvae"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rex-reasoning-aware-and-grounded-explanation","slug":"rex-reasoning-aware-and-grounded-explanation","title":"REX: Reasoning-aware and Grounded Explanation","date":"2022-03-11","arxiv_id":"2203.06107","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 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","sample_list":"/paper/rex-reasoning-aware-and-grounded-explanation#ran","syntology_url":"https://syntology.ai/paper/2203.06107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06107"}},"official":{"repos":["szzexpoi/rex"],"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/nlx-gpt-a-model-for-natural-language","slug":"nlx-gpt-a-model-for-natural-language","title":"NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks","date":"2022-03-09","arxiv_id":"2203.05081","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-based-reinforcement-learning-for","slug":"curriculum-based-reinforcement-learning-for","title":"Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration","date":"2022-03-08","arxiv_id":"2203.04166","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-entity-1","slug":"deep-reinforcement-learning-for-entity-1","title":"Deep Reinforcement Learning for Entity Alignment","date":"2022-03-07","arxiv_id":"2203.03315","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"10 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-reinforcement-learning-for-entity-1#ran","syntology_url":"https://syntology.ai/paper/2203.03315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03315"}},"official":{"repos":["guolingbing/rlea"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/opengridgym-an-open-source-ai-friendly","slug":"opengridgym-an-open-source-ai-friendly","title":"OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation","date":"2022-03-06","arxiv_id":"2203.04410","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-human-decision-making-with-ai","slug":"boosting-human-decision-making-with-ai","title":"Boosting human decision-making with AI-generated decision aids","date":"2022-03-05","arxiv_id":"2203.02776","repositories_listed":1,"syntology":null},{"url":"/paper/dime-fine-grained-interpretations-of","slug":"dime-fine-grained-interpretations-of","title":"DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations","date":"2022-03-03","arxiv_id":"2203.02013","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dime-fine-grained-interpretations-of#ran","syntology_url":"https://syntology.ai/paper/2203.02013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02013"}},"official":{"repos":["lvyiwei1/dime"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/random-quantum-neural-networks-rqnn-for-noisy","slug":"random-quantum-neural-networks-rqnn-for-noisy","title":"Random Quantum Neural Networks (RQNN) for Noisy Image Recognition","date":"2022-03-03","arxiv_id":"2203.01764","repositories_listed":1,"syntology":null},{"url":"/paper/ai-planning-annotation-for-sample-efficient","slug":"ai-planning-annotation-for-sample-efficient","title":"Hierarchical Reinforcement Learning with AI Planning Models","date":"2022-03-01","arxiv_id":"2203.00669","repositories_listed":1,"syntology":null},{"url":"/paper/recovery-of-missing-sensor-data-by","slug":"recovery-of-missing-sensor-data-by","title":"Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals","date":"2022-03-01","arxiv_id":"2203.00418","repositories_listed":1,"syntology":null},{"url":"/paper/text-t-3-omvp-a-transformer-based-time-and","slug":"text-t-3-omvp-a-transformer-based-time-and","title":"$ \\text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area","date":"2022-03-01","arxiv_id":"2203.00183","repositories_listed":1,"syntology":null},{"url":"/paper/lisa-learning-interpretable-skill","slug":"lisa-learning-interpretable-skill","title":"LISA: Learning Interpretable Skill Abstractions from Language","date":"2022-02-28","arxiv_id":"2203.00054","repositories_listed":1,"syntology":null},{"url":"/paper/robust-probabilistic-time-series-forecasting","slug":"robust-probabilistic-time-series-forecasting","title":"Robust Probabilistic Time Series Forecasting","date":"2022-02-24","arxiv_id":"2202.11910","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainr-uncertainty-quantification-of-end","slug":"uncertainr-uncertainty-quantification-of-end","title":"UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography","date":"2022-02-22","arxiv_id":"2202.10847","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/uncertainr-uncertainty-quantification-of-end#ran","syntology_url":"https://syntology.ai/paper/2202.10847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.10847"}},"official":{"repos":["bobby-he/uncertainr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/translational-quantum-machine-intelligence","slug":"translational-quantum-machine-intelligence","title":"Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology","date":"2022-02-21","arxiv_id":"2202.10919","repositories_listed":1,"syntology":null},{"url":"/paper/bayes-optimal-classifiers-under-group","slug":"bayes-optimal-classifiers-under-group","title":"Bayes-Optimal Classifiers under Group Fairness","date":"2022-02-20","arxiv_id":"2202.09724","repositories_listed":1,"syntology":null},{"url":"/paper/autoscore-ordinal-an-interpretable-machine","slug":"autoscore-ordinal-an-interpretable-machine","title":"AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes","date":"2022-02-17","arxiv_id":"2202.08407","repositories_listed":1,"syntology":null},{"url":"/paper/handcrafted-histological-transformer-h2t","slug":"handcrafted-histological-transformer-h2t","title":"Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images","date":"2022-02-14","arxiv_id":"2202.07001","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/handcrafted-histological-transformer-h2t#ran","syntology_url":"https://syntology.ai/paper/2202.07001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.07001"}},"official":{"repos":["vqdang/h2t"],"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-reward-models-for-cooperative","slug":"learning-reward-models-for-cooperative","title":"Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search","date":"2022-02-14","arxiv_id":"2202.06443","repositories_listed":1,"syntology":null},{"url":"/paper/uncalibrated-models-can-improve-human-ai","slug":"uncalibrated-models-can-improve-human-ai","title":"Uncalibrated Models Can Improve Human-AI Collaboration","date":"2022-02-12","arxiv_id":"2202.05983","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uncalibrated-models-can-improve-human-ai#ran","syntology_url":"https://syntology.ai/paper/2202.05983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.05983"}},"official":{"repos":["kailas-v/human-ai-interactions"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/explainable-covid-19-infections","slug":"explainable-covid-19-infections","title":"Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels","date":"2022-02-11","arxiv_id":"2202.07422","repositories_listed":1,"syntology":null},{"url":"/paper/the-leap-to-ordinal-functional-prognosis","slug":"the-leap-to-ordinal-functional-prognosis","title":"The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach","date":"2022-02-10","arxiv_id":"2202.04801","repositories_listed":1,"syntology":null},{"url":"/paper/towards-the-automated-large-scale","slug":"towards-the-automated-large-scale","title":"Towards the automated large-scale reconstruction of past road networks from historical maps","date":"2022-02-10","arxiv_id":"2202.04883","repositories_listed":1,"syntology":null}],"record_sha256":"065cedfc3778f48cc769e8e5af934d974eef3d7d7db819917fa739f3ddd83fa6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}