{"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/29","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":29,"pages_in_order":124,"rows_per_page":100,"rows":[2801,2900],"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/28","next":"/task/decision-making/papers/30","papers":[{"url":"/paper/read-watch-and-move-reinforcement-learning","slug":"read-watch-and-move-reinforcement-learning","title":"Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos","date":"2019-01-21","arxiv_id":"1901.06829","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-interpretability-and-trust-in","slug":"quantifying-interpretability-and-trust-in","title":"Quantifying Interpretability and Trust in Machine Learning Systems","date":"2019-01-20","arxiv_id":"1901.08558","repositories_listed":1,"syntology":null},{"url":"/paper/the-bayesian-prophet-a-low-regret-framework","slug":"the-bayesian-prophet-a-low-regret-framework","title":"The Bayesian Prophet: A Low-Regret Framework for Online Decision Making","date":"2019-01-15","arxiv_id":"1901.05028","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-recurrent-q-network-towards-self","slug":"a-deep-recurrent-q-network-towards-self","title":"A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architecture","date":"2019-01-13","arxiv_id":"1901.04011","repositories_listed":1,"syntology":null},{"url":"/paper/causal-discovery-with-attention-based","slug":"causal-discovery-with-attention-based","title":"Causal Discovery with Attention-Based Convolutional Neural Networks","date":"2019-01-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/constrained-optimization-under-uncertainty","slug":"constrained-optimization-under-uncertainty","title":"Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games","date":"2019-01-03","arxiv_id":"1901.00942","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-interpret-atari-agents","slug":"learn-to-interpret-atari-agents","title":"Learn to Interpret Atari Agents","date":"2018-12-29","arxiv_id":"1812.11276","repositories_listed":1,"syntology":null},{"url":"/paper/resolving-the-measurement-uncertainty-paradox","slug":"resolving-the-measurement-uncertainty-paradox","title":"Resolving the measurement uncertainty paradox in ecological management","date":"2018-12-28","arxiv_id":"1812.11184","repositories_listed":1,"syntology":null},{"url":"/paper/goal-based-course-recommendation","slug":"goal-based-course-recommendation","title":"Goal-based Course Recommendation","date":"2018-12-25","arxiv_id":"1812.10078","repositories_listed":1,"syntology":null},{"url":"/paper/learning-loop-invariants-for-program","slug":"learning-loop-invariants-for-program","title":"Learning Loop Invariants for Program Verification","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/structural-causal-bandits-where-to-intervene","slug":"structural-causal-bandits-where-to-intervene","title":"Structural Causal Bandits: Where to Intervene?","date":"2018-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/clear-the-fog-combat-value-assessment-in","slug":"clear-the-fog-combat-value-assessment-in","title":"Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders","date":"2018-11-30","arxiv_id":"1811.12627","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-under-unawareness-assessing","slug":"fairness-under-unawareness-assessing","title":"Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved","date":"2018-11-27","arxiv_id":"1811.11154","repositories_listed":1,"syntology":null},{"url":"/paper/lstm-neural-network-for-textual-ngrams","slug":"lstm-neural-network-for-textual-ngrams","title":"LSTM Neural Network for Textual Ngrams","date":"2018-11-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-gray-box-interpretable-visual-debugging","slug":"a-gray-box-interpretable-visual-debugging","title":"A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model","date":"2018-11-20","arxiv_id":"1811.08374","repositories_listed":1,"syntology":null},{"url":"/paper/deepseenet-a-deep-learning-model-for","slug":"deepseenet-a-deep-learning-model-for","title":"DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs","date":"2018-11-19","arxiv_id":"1811.07492","repositories_listed":1,"syntology":null},{"url":"/paper/learning-actionable-representations-with-goal","slug":"learning-actionable-representations-with-goal","title":"Learning Actionable Representations with Goal-Conditioned Policies","date":"2018-11-19","arxiv_id":"1811.07819","repositories_listed":1,"syntology":null},{"url":"/paper/deep-bayesian-inversion","slug":"deep-bayesian-inversion","title":"Deep Bayesian Inversion","date":"2018-11-14","arxiv_id":"1811.05910","repositories_listed":1,"syntology":null},{"url":"/paper/trolleymod-v10-an-open-source-simulation-and","slug":"trolleymod-v10-an-open-source-simulation-and","title":"TrolleyMod v1.0: An Open-Source Simulation and Data-Collection Platform for Ethical Decision Making in Autonomous Vehicles","date":"2018-11-14","arxiv_id":"1811.05594","repositories_listed":1,"syntology":null},{"url":"/paper/deep-item-based-collaborative-filtering-for","slug":"deep-item-based-collaborative-filtering-for","title":"Deep Item-based Collaborative Filtering for Top-N Recommendation","date":"2018-11-11","arxiv_id":"1811.04392","repositories_listed":1,"syntology":null},{"url":"/paper/langevin-gradient-parallel-tempering-for","slug":"langevin-gradient-parallel-tempering-for","title":"Langevin-gradient parallel tempering for Bayesian neural learning","date":"2018-11-11","arxiv_id":"1811.04343","repositories_listed":1,"syntology":null},{"url":"/paper/localization-and-perception-for-control-and","slug":"localization-and-perception-for-control-and","title":"Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/heteroscedastic-bandits-with-reneging","slug":"heteroscedastic-bandits-with-reneging","title":"Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging","date":"2018-10-29","arxiv_id":"1810.12418","repositories_listed":1,"syntology":null},{"url":"/paper/a-gray-box-interpretable-visual-debugging-1","slug":"a-gray-box-interpretable-visual-debugging-1","title":"A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model","date":"2018-10-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/factorized-machine-self-confidence-for","slug":"factorized-machine-self-confidence-for","title":"Factorized Machine Self-Confidence for Decision-Making Agents","date":"2018-10-15","arxiv_id":"1810.06519","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-multimodal-dynamic-spatiotemporal","slug":"modeling-multimodal-dynamic-spatiotemporal","title":"The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs","date":"2018-10-14","arxiv_id":"1810.05993","repositories_listed":1,"syntology":null},{"url":"/paper/what-made-you-do-this-understanding-black-box","slug":"what-made-you-do-this-understanding-black-box","title":"What made you do this? Understanding black-box decisions with sufficient input subsets","date":"2018-10-09","arxiv_id":"1810.03805","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":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) · 0 unverified","sample_list":"/paper/what-made-you-do-this-understanding-black-box#ran","syntology_url":"https://syntology.ai/paper/1810.03805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03805"}},"official":{"repos":["b-carter/SufficientInputSubsets"],"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/from-soft-classifiers-to-hard-decisions-how","slug":"from-soft-classifiers-to-hard-decisions-how","title":"From Soft Classifiers to Hard Decisions: How fair can we be?","date":"2018-10-03","arxiv_id":"1810.02003","repositories_listed":1,"syntology":null},{"url":"/paper/challenges-of-using-text-classifiers-for","slug":"challenges-of-using-text-classifiers-for","title":"Challenges of Using Text Classifiers for Causal Inference","date":"2018-10-01","arxiv_id":"1810.00956","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/challenges-of-using-text-classifiers-for#ran","syntology_url":"https://syntology.ai/paper/1810.00956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00956"}},"official":{"repos":["zachwooddoughty/emnlp2018-causal"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/commonsense-justification-for-action","slug":"commonsense-justification-for-action","title":"Commonsense Justification for Action Explanation","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multiwoz-a-large-scale-multi-domain-wizard-of-1","slug":"multiwoz-a-large-scale-multi-domain-wizard-of-1","title":"MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-sequence-labeling-with-actor-critic","slug":"efficient-sequence-labeling-with-actor-critic","title":"Efficient Sequence Labeling with Actor-Critic Training","date":"2018-09-30","arxiv_id":"1810.00428","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-learning-with-corrective-feedback","slug":"interactive-learning-with-corrective-feedback","title":"Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks","date":"2018-09-30","arxiv_id":"1810.00466","repositories_listed":1,"syntology":null},{"url":"/paper/eddi-efficient-dynamic-discovery-of-high","slug":"eddi-efficient-dynamic-discovery-of-high","title":"EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE","date":"2018-09-28","arxiv_id":"1809.11142","repositories_listed":1,"syntology":null},{"url":"/paper/better-safe-than-sorry-evidence-accumulation","slug":"better-safe-than-sorry-evidence-accumulation","title":"Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning","date":"2018-09-24","arxiv_id":"1809.09147","repositories_listed":1,"syntology":null},{"url":"/paper/lener-br-a-dataset-for-named-entity","slug":"lener-br-a-dataset-for-named-entity","title":"LeNER-Br: a Dataset for Named Entity Recognition in Brazilian Legal Text","date":"2018-09-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-logic-programming-with-beta","slug":"probabilistic-logic-programming-with-beta","title":"Probabilistic Logic Programming with Beta-Distributed Random Variables","date":"2018-09-20","arxiv_id":"1809.07888","repositories_listed":1,"syntology":null},{"url":"/paper/effective-deep-learning-for-semantic","slug":"effective-deep-learning-for-semantic","title":"Effective Deep Learning for Semantic Segmentation Based Bleeding Zone Detection in Capsule Endoscopy Images","date":"2018-09-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/application-of-machine-learning-in-rock","slug":"application-of-machine-learning-in-rock","title":"Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation","date":"2018-08-29","arxiv_id":"1808.09856","repositories_listed":1,"syntology":null},{"url":"/paper/on-feature-selection-and-evaluation-of","slug":"on-feature-selection-and-evaluation-of","title":"On feature selection and evaluation of transportation mode prediction strategies","date":"2018-08-09","arxiv_id":"1808.03096","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-importance-sampling-bandits","slug":"sequential-importance-sampling-bandits","title":"Sequential Monte Carlo Bandits","date":"2018-08-08","arxiv_id":"1808.02933","repositories_listed":1,"syntology":null},{"url":"/paper/visualizing-convolutional-networks-for-mri","slug":"visualizing-convolutional-networks-for-mri","title":"Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease","date":"2018-08-08","arxiv_id":"1808.02874","repositories_listed":1,"syntology":null},{"url":"/paper/style-detection-for-free-verse-poetry-from","slug":"style-detection-for-free-verse-poetry-from","title":"Style Detection for Free Verse Poetry from Text and Speech","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/attend-before-you-act-leveraging-human-visual","slug":"attend-before-you-act-leveraging-human-visual","title":"Attend Before you Act: Leveraging human visual attention for continual learning","date":"2018-07-25","arxiv_id":"1807.09664","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-neural-computation-via-stack","slug":"explainable-neural-computation-via-stack","title":"Explainable Neural Computation via Stack Neural Module Networks","date":"2018-07-23","arxiv_id":"1807.08556","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"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 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) · 2 unverified","sample_list":"/paper/explainable-neural-computation-via-stack#ran","syntology_url":"https://syntology.ai/paper/1807.08556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08556"}},"official":null}},{"url":"/paper/knowledge-based-transfer-learning-explanation","slug":"knowledge-based-transfer-learning-explanation","title":"Knowledge-based Transfer Learning Explanation","date":"2018-07-22","arxiv_id":"1807.08372","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-swarm-systems","slug":"deep-reinforcement-learning-for-swarm-systems","title":"Deep Reinforcement Learning for Swarm Systems","date":"2018-07-17","arxiv_id":"1807.06613","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-listen-read-and-follow-score","slug":"learning-to-listen-read-and-follow-score","title":"Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game","date":"2018-07-17","arxiv_id":"1807.06391","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"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; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-to-listen-read-and-follow-score#ran","syntology_url":"https://syntology.ai/paper/1807.06391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06391"}},"official":{"repos":["CPJKU/score_following_game"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/automatically-composing-representation","slug":"automatically-composing-representation","title":"Automatically Composing Representation Transformations as a Means for Generalization","date":"2018-07-12","arxiv_id":"1807.04640","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/automatically-composing-representation#ran","syntology_url":"https://syntology.ai/paper/1807.04640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.04640"}},"official":{"repos":["mbchang/crl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/automated-and-interpretable-patient-ecg","slug":"automated-and-interpretable-patient-ecg","title":"Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery","date":"2018-07-06","arxiv_id":"1807.02569","repositories_listed":1,"syntology":null},{"url":"/paper/automated-directed-fairness-testing","slug":"automated-directed-fairness-testing","title":"Automated Directed Fairness Testing","date":"2018-07-02","arxiv_id":"1807.00468","repositories_listed":1,"syntology":null},{"url":"/paper/neural-program-synthesis-from-diverse","slug":"neural-program-synthesis-from-diverse","title":"Neural Program Synthesis from Diverse Demonstration Videos","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-surgical","slug":"deep-reinforcement-learning-for-surgical","title":"Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification","date":"2018-06-21","arxiv_id":"1806.08089","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-chinese-zero","slug":"deep-reinforcement-learning-for-chinese-zero","title":"Deep Reinforcement Learning for Chinese Zero pronoun Resolution","date":"2018-06-10","arxiv_id":"1806.03711","repositories_listed":1,"syntology":null},{"url":"/paper/the-impact-of-humanoid-affect-expression-on","slug":"the-impact-of-humanoid-affect-expression-on","title":"The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting","date":"2018-06-10","arxiv_id":"1806.03671","repositories_listed":1,"syntology":null},{"url":"/paper/pots-protective-optimization-technologies","slug":"pots-protective-optimization-technologies","title":"POTs: Protective Optimization Technologies","date":"2018-06-07","arxiv_id":"1806.02711","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/pots-protective-optimization-technologies#ran","syntology_url":"https://syntology.ai/paper/1806.02711","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02711"}},"official":{"repos":["spring-epfl/pots"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-variational-reinforcement-learning-for","slug":"deep-variational-reinforcement-learning-for","title":"Deep Variational Reinforcement Learning for POMDPs","date":"2018-06-06","arxiv_id":"1806.02426","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/deep-variational-reinforcement-learning-for#ran","syntology_url":"https://syntology.ai/paper/1806.02426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02426"}},"official":{"repos":["maximilianigl/DVRL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/playing-atari-with-six-neurons","slug":"playing-atari-with-six-neurons","title":"Playing Atari with Six Neurons","date":"2018-06-04","arxiv_id":"1806.01363","repositories_listed":1,"syntology":null},{"url":"/paper/shallow-decision-making-analysis-in-general","slug":"shallow-decision-making-analysis-in-general","title":"Shallow decision-making analysis in General Video Game Playing","date":"2018-06-04","arxiv_id":"1806.01151","repositories_listed":1,"syntology":null},{"url":"/paper/calibrating-deep-convolutional-gaussian","slug":"calibrating-deep-convolutional-gaussian","title":"Calibrating Deep Convolutional Gaussian Processes","date":"2018-05-26","arxiv_id":"1805.10522","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/calibrating-deep-convolutional-gaussian#ran","syntology_url":"https://syntology.ai/paper/1805.10522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10522"}},"official":null}},{"url":"/paper/enhancing-the-accuracy-and-fairness-of-human","slug":"enhancing-the-accuracy-and-fairness-of-human","title":"Enhancing the Accuracy and Fairness of Human Decision Making","date":"2018-05-25","arxiv_id":"1805.10318","repositories_listed":1,"syntology":null},{"url":"/paper/excitation-dropout-encouraging-plasticity-in","slug":"excitation-dropout-encouraging-plasticity-in","title":"Excitation Dropout: Encouraging Plasticity in Deep Neural Networks","date":"2018-05-23","arxiv_id":"1805.09092","repositories_listed":1,"syntology":null},{"url":"/paper/a-lyapunov-based-approach-to-safe","slug":"a-lyapunov-based-approach-to-safe","title":"A Lyapunov-based Approach to Safe Reinforcement Learning","date":"2018-05-20","arxiv_id":"1805.07708","repositories_listed":1,"syntology":null},{"url":"/paper/machine-teaching-for-inverse-reinforcement","slug":"machine-teaching-for-inverse-reinforcement","title":"Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications","date":"2018-05-20","arxiv_id":"1805.07687","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/machine-teaching-for-inverse-reinforcement#ran","syntology_url":"https://syntology.ai/paper/1805.07687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07687"}},"official":{"repos":["dsbrown1331/machine-teaching-irl"],"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/unsupervised-video-object-segmentation-for","slug":"unsupervised-video-object-segmentation-for","title":"Unsupervised Video Object Segmentation for Deep Reinforcement Learning","date":"2018-05-20","arxiv_id":"1805.07780","repositories_listed":1,"syntology":null},{"url":"/paper/ask-no-more-deciding-when-to-guess-in","slug":"ask-no-more-deciding-when-to-guess-in","title":"Ask No More: Deciding when to guess in referential visual dialogue","date":"2018-05-17","arxiv_id":"1805.06960","repositories_listed":1,"syntology":null},{"url":"/paper/a-tree-search-algorithm-for-sequence-labeling","slug":"a-tree-search-algorithm-for-sequence-labeling","title":"A Tree Search Algorithm for Sequence Labeling","date":"2018-04-29","arxiv_id":"1804.10911","repositories_listed":1,"syntology":null},{"url":"/paper/revealing-patterns-in-hiv-viral-load-data-and","slug":"revealing-patterns-in-hiv-viral-load-data-and","title":"Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method","date":"2018-04-25","arxiv_id":"1804.11195","repositories_listed":1,"syntology":null},{"url":"/paper/perview-a-framework-for-personalized-review","slug":"perview-a-framework-for-personalized-review","title":"PeRView: A Framework for Personalized Review Selection Using Micro-Reviews","date":"2018-04-23","arxiv_id":"1804.08234","repositories_listed":1,"syntology":null},{"url":"/paper/on-learning-intrinsic-rewards-for-policy","slug":"on-learning-intrinsic-rewards-for-policy","title":"On Learning Intrinsic Rewards for Policy Gradient Methods","date":"2018-04-17","arxiv_id":"1804.06459","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-learning-intrinsic-rewards-for-policy#ran","syntology_url":"https://syntology.ai/paper/1804.06459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06459"}},"official":{"repos":["Hwhitetooth/lirpg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-and-interpretable-one-class-svms","slug":"scalable-and-interpretable-one-class-svms","title":"Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features","date":"2018-04-13","arxiv_id":"1804.04888","repositories_listed":1,"syntology":null},{"url":"/paper/a-bi-population-particle-swarm-optimizer-for","slug":"a-bi-population-particle-swarm-optimizer-for","title":"A Bi-population Particle Swarm Optimizer for Learning Automata based Slow Intelligent System","date":"2018-04-03","arxiv_id":"1804.00768","repositories_listed":1,"syntology":null},{"url":"/paper/inference-in-probabilistic-graphical-models","slug":"inference-in-probabilistic-graphical-models","title":"Inference in Probabilistic Graphical Models by Graph Neural Networks","date":"2018-03-21","arxiv_id":"1803.07710","repositories_listed":1,"syntology":null},{"url":"/paper/minimal-i-map-mcmc-for-scalable-structure","slug":"minimal-i-map-mcmc-for-scalable-structure","title":"Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models","date":"2018-03-15","arxiv_id":"1803.05554","repositories_listed":1,"syntology":null},{"url":"/paper/mixed-integer-convex-nonlinear-optimization","slug":"mixed-integer-convex-nonlinear-optimization","title":"Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded","date":"2018-03-02","arxiv_id":"1803.00952","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-to-rank-in-e-commerce","slug":"reinforcement-learning-to-rank-in-e-commerce","title":"Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application","date":"2018-03-02","arxiv_id":"1803.00710","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-human-priors-for-playing-video","slug":"investigating-human-priors-for-playing-video","title":"Investigating Human Priors for Playing Video Games","date":"2018-02-28","arxiv_id":"1802.10217","repositories_listed":1,"syntology":null},{"url":"/paper/structured-control-nets-for-deep","slug":"structured-control-nets-for-deep","title":"Structured Control Nets for Deep Reinforcement Learning","date":"2018-02-22","arxiv_id":"1802.08311","repositories_listed":1,"syntology":null},{"url":"/paper/manipulating-and-measuring-model","slug":"manipulating-and-measuring-model","title":"Manipulating and Measuring Model Interpretability","date":"2018-02-21","arxiv_id":"1802.07810","repositories_listed":1,"syntology":null},{"url":"/paper/ordered-preference-elicitation-strategies-for","slug":"ordered-preference-elicitation-strategies-for","title":"Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making","date":"2018-02-21","arxiv_id":"1802.07606","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/ordered-preference-elicitation-strategies-for#ran","syntology_url":"https://syntology.ai/paper/1802.07606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07606"}},"official":{"repos":["lmzintgraf/gp_pref_elicit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/from-gameplay-to-symbolic-reasoning-learning","slug":"from-gameplay-to-symbolic-reasoning-learning","title":"From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero","date":"2018-02-14","arxiv_id":"1802.05340","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-decision-making-when-interpretable","slug":"hybrid-decision-making-when-interpretable","title":"Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute","date":"2018-02-12","arxiv_id":"1802.04346","repositories_listed":1,"syntology":null},{"url":"/paper/utility-decomposition-with-deep-corrections","slug":"utility-decomposition-with-deep-corrections","title":"Decomposition Methods with Deep Corrections for Reinforcement Learning","date":"2018-02-06","arxiv_id":"1802.01772","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-representation-selection-in","slug":"adaptive-representation-selection-in","title":"Contextual Bandit with Adaptive Feature Extraction","date":"2018-02-03","arxiv_id":"1802.00981","repositories_listed":1,"syntology":null},{"url":"/paper/logically-constrained-reinforcement-learning","slug":"logically-constrained-reinforcement-learning","title":"Logically-Constrained Reinforcement Learning","date":"2018-01-24","arxiv_id":"1801.08099","repositories_listed":1,"syntology":null},{"url":"/paper/a-human-grounded-evaluation-benchmark-for","slug":"a-human-grounded-evaluation-benchmark-for","title":"A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning","date":"2018-01-16","arxiv_id":"1801.05075","repositories_listed":1,"syntology":null},{"url":"/paper/learning-structural-weight-uncertainty-for","slug":"learning-structural-weight-uncertainty-for","title":"Learning Structural Weight Uncertainty for Sequential Decision-Making","date":"2017-12-30","arxiv_id":"1801.00085","repositories_listed":1,"syntology":null},{"url":"/paper/diversifying-support-vector-machines-for","slug":"diversifying-support-vector-machines-for","title":"Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts","date":"2017-12-22","arxiv_id":"1712.08493","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-text-generation-and-planning-for","slug":"hierarchical-text-generation-and-planning-for","title":"Hierarchical Text Generation and Planning for Strategic Dialogue","date":"2017-12-15","arxiv_id":"1712.05846","repositories_listed":1,"syntology":null},{"url":"/paper/risk-sensitive-inverse-reinforcement-learning","slug":"risk-sensitive-inverse-reinforcement-learning","title":"Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods","date":"2017-11-28","arxiv_id":"1711.10055","repositories_listed":1,"syntology":null},{"url":"/paper/bootstrap-robust-prescriptive-analytics","slug":"bootstrap-robust-prescriptive-analytics","title":"Bootstrap Robust Prescriptive Analytics","date":"2017-11-27","arxiv_id":"1711.09974","repositories_listed":1,"syntology":null},{"url":"/paper/classification-with-costly-features-using","slug":"classification-with-costly-features-using","title":"Classification with Costly Features using Deep Reinforcement Learning","date":"2017-11-20","arxiv_id":"1711.07364","repositories_listed":1,"syntology":null},{"url":"/paper/does-mitigating-mls-impact-disparity-require","slug":"does-mitigating-mls-impact-disparity-require","title":"Does mitigating ML's impact disparity require treatment disparity?","date":"2017-11-19","arxiv_id":"1711.07076","repositories_listed":1,"syntology":null},{"url":"/paper/anonymous-hedonic-game-for-task-allocation-in","slug":"anonymous-hedonic-game-for-task-allocation-in","title":"Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System","date":"2017-11-18","arxiv_id":"1711.06871","repositories_listed":1,"syntology":null},{"url":"/paper/predict-responsibly-improving-fairness-and","slug":"predict-responsibly-improving-fairness-and","title":"Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer","date":"2017-11-17","arxiv_id":"1711.06664","repositories_listed":1,"syntology":null},{"url":"/paper/learning-and-visualizing-localized-geometric","slug":"learning-and-visualizing-localized-geometric","title":"Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes","date":"2017-11-13","arxiv_id":"1711.04851","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-knee-osteoarthritis-diagnosis-from","slug":"automatic-knee-osteoarthritis-diagnosis-from","title":"Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach","date":"2017-10-29","arxiv_id":"1710.10589","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-probabilistic-model-based-planning","slug":"multimodal-probabilistic-model-based-planning","title":"Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction","date":"2017-10-25","arxiv_id":"1710.09483","repositories_listed":1,"syntology":null},{"url":"/paper/decomposition-of-uncertainty-in-bayesian-deep","slug":"decomposition-of-uncertainty-in-bayesian-deep","title":"Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning","date":"2017-10-19","arxiv_id":"1710.07283","repositories_listed":1,"syntology":null},{"url":"/paper/smooth-pinball-neural-network-for","slug":"smooth-pinball-neural-network-for","title":"Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power","date":"2017-10-04","arxiv_id":"1710.01720","repositories_listed":1,"syntology":null}],"record_sha256":"57c05cede0aea70f29cb60e25c911d8943605b63c73085de4cf5509a8f30a5b5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}