{"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/scheduling/papers/4","list_of":"/task/scheduling","task":"Scheduling","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":4,"pages_in_order":32,"rows_per_page":100,"rows":[301,400],"of":3104,"counts":{"archive_papers_tagged":3104,"with_a_code_link":574,"where_syntology_ran_a_sample":107,"not_listed_spam_title":0,"listed":3104,"listed_where_code_ran":107,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":94,"every_run_a_failure_of_syntologys_instrument":13,"listed_with_a_run_with_no_instrument_failure":94,"listed_every_run_a_failure_of_syntologys_instrument":13,"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/scheduling","prev":"/task/scheduling/papers/3","next":"/task/scheduling/papers/5","papers":[{"url":"/paper/latency-aware-unified-dynamic-networks-for","slug":"latency-aware-unified-dynamic-networks-for","title":"Latency-aware Unified Dynamic Networks for Efficient Image Recognition","date":"2023-08-30","arxiv_id":"2308.15949","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/latency-aware-unified-dynamic-networks-for#ran","syntology_url":"https://syntology.ai/paper/2308.15949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15949"}},"official":{"repos":["leaplabthu/laudnet"],"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/edge-generation-scheduling-for-dag-tasks","slug":"edge-generation-scheduling-for-dag-tasks","title":"Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning","date":"2023-08-28","arxiv_id":"2308.14647","repositories_listed":1,"syntology":null},{"url":"/paper/job-shop-scheduling-benchmark-environments","slug":"job-shop-scheduling-benchmark-environments","title":"Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods","date":"2023-08-24","arxiv_id":"2308.12794","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/job-shop-scheduling-benchmark-environments#ran","syntology_url":"https://syntology.ai/paper/2308.12794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12794"}},"official":{"repos":["ai-for-decision-making-tue/job_shop_scheduling_benchmark"],"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/equitable-restless-multi-armed-bandits-a","slug":"equitable-restless-multi-armed-bandits-a","title":"Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health","date":"2023-08-17","arxiv_id":"2308.09726","repositories_listed":1,"syntology":null},{"url":"/paper/shared-memory-contention-aware-concurrent-dnn","slug":"shared-memory-contention-aware-concurrent-dnn","title":"Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips","date":"2023-08-10","arxiv_id":"2308.05869","repositories_listed":1,"syntology":null},{"url":"/paper/stabilizing-training-with-soft-dynamic-time","slug":"stabilizing-training-with-soft-dynamic-time","title":"Stabilizing Training with Soft Dynamic Time Warping: A Case Study for Pitch Class Estimation with Weakly Aligned Targets","date":"2023-08-10","arxiv_id":"2308.05429","repositories_listed":1,"syntology":null},{"url":"/paper/job-shop-scheduling-via-deep-reinforcement","slug":"job-shop-scheduling-via-deep-reinforcement","title":"Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach","date":"2023-08-03","arxiv_id":"2308.01797","repositories_listed":1,"syntology":null},{"url":"/paper/intent-aware-radio-resource-scheduling-in-a","slug":"intent-aware-radio-resource-scheduling-in-a","title":"Intent-aware Radio Resource Scheduling in a RAN Slicing Scenario using Reinforcement Learning","date":"2023-08-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pm-gati-shakti-advancing-india-s-energy","slug":"pm-gati-shakti-advancing-india-s-energy","title":"PM-Gati Shakti: Advancing India's Energy Future through Demand Forecasting -- A Case Study","date":"2023-07-30","arxiv_id":"2308.07320","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-based-adaptation-and","slug":"reinforcement-learning-based-adaptation-and","title":"Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASH","date":"2023-07-25","arxiv_id":"2308.11621","repositories_listed":1,"syntology":null},{"url":"/paper/rosko-row-skipping-outer-products-for-sparse","slug":"rosko-row-skipping-outer-products-for-sparse","title":"Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels","date":"2023-07-08","arxiv_id":"2307.03930","repositories_listed":1,"syntology":null},{"url":"/paper/multi-objective-deep-reinforcement-learning-1","slug":"multi-objective-deep-reinforcement-learning-1","title":"Multi-objective Deep Reinforcement Learning for Mobile Edge Computing","date":"2023-07-05","arxiv_id":"2307.14346","repositories_listed":1,"syntology":null},{"url":"/paper/renewable-energy-management-in-smart-home-1","slug":"renewable-energy-management-in-smart-home-1","title":"Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network","date":"2023-07-04","arxiv_id":"2307.01622","repositories_listed":1,"syntology":null},{"url":"/paper/skiros2-a-skill-based-robot-control-platform","slug":"skiros2-a-skill-based-robot-control-platform","title":"SkiROS2: A skill-based Robot Control Platform for ROS","date":"2023-06-29","arxiv_id":"2306.17030","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-machine-translation-corpus-1","slug":"efficient-machine-translation-corpus-1","title":"Efficient Machine Translation Corpus Generation","date":"2023-06-20","arxiv_id":"2306.11838","repositories_listed":1,"syntology":null},{"url":"/paper/decentralized-social-navigation-with-non","slug":"decentralized-social-navigation-with-non","title":"Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization","date":"2023-06-15","arxiv_id":"2306.08815","repositories_listed":1,"syntology":null},{"url":"/paper/an-end-to-end-reinforcement-learning-approach","slug":"an-end-to-end-reinforcement-learning-approach","title":"An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming","date":"2023-06-09","arxiv_id":"2306.05747","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-end-to-end-reinforcement-learning-approach#ran","syntology_url":"https://syntology.ai/paper/2306.05747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05747"}},"official":{"repos":["ingambe/End2End-Job-Shop-Scheduling-CP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/policy-based-self-competition-for-planning","slug":"policy-based-self-competition-for-planning","title":"Policy-Based Self-Competition for Planning Problems","date":"2023-06-07","arxiv_id":"2306.04403","repositories_listed":1,"syntology":null},{"url":"/paper/onsite-job-scheduling-by-adaptive-genetic","slug":"onsite-job-scheduling-by-adaptive-genetic","title":"Onsite Job Scheduling by Adaptive Genetic Algorithm","date":"2023-06-04","arxiv_id":"2306.02296","repositories_listed":1,"syntology":null},{"url":"/paper/symmetric-exploration-in-combinatorial","slug":"symmetric-exploration-in-combinatorial","title":"Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization","date":"2023-06-02","arxiv_id":"2306.01276","repositories_listed":1,"syntology":null},{"url":"/paper/fast-variational-block-sparse-bayesian","slug":"fast-variational-block-sparse-bayesian","title":"Fast Variational Block-Sparse Bayesian Learning","date":"2023-06-01","arxiv_id":"2306.00442","repositories_listed":1,"syntology":null},{"url":"/paper/plasma-making-small-language-models-better","slug":"plasma-making-small-language-models-better","title":"PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning","date":"2023-05-31","arxiv_id":"2305.19472","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/plasma-making-small-language-models-better#ran","syntology_url":"https://syntology.ai/paper/2305.19472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19472"}},"official":{"repos":["allenai/plasma"],"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/nested-diffusion-processes-for-anytime-image","slug":"nested-diffusion-processes-for-anytime-image","title":"Nested Diffusion Processes for Anytime Image Generation","date":"2023-05-30","arxiv_id":"2305.19066","repositories_listed":1,"syntology":null},{"url":"/paper/sheetcopilot-bringing-software-productivity","slug":"sheetcopilot-bringing-software-productivity","title":"SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models","date":"2023-05-30","arxiv_id":"2305.19308","repositories_listed":1,"syntology":null},{"url":"/paper/value-of-information-analysis-for","slug":"value-of-information-analysis-for","title":"Value of Information Analysis for rationalising information gathering in building energy analysis","date":"2023-05-25","arxiv_id":"2305.16117","repositories_listed":1,"syntology":null},{"url":"/paper/fedzero-leveraging-renewable-excess-energy-in","slug":"fedzero-leveraging-renewable-excess-energy-in","title":"FedZero: Leveraging Renewable Excess Energy in Federated Learning","date":"2023-05-24","arxiv_id":"2305.15092","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fedzero-leveraging-renewable-excess-energy-in#ran","syntology_url":"https://syntology.ai/paper/2305.15092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15092"}},"official":{"repos":["dos-group/fedzero"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/constrained-reinforcement-learning-for-4","slug":"constrained-reinforcement-learning-for-4","title":"Constrained Reinforcement Learning for Dynamic Material Handling","date":"2023-05-23","arxiv_id":"2305.13824","repositories_listed":1,"syntology":null},{"url":"/paper/response-length-perception-and-sequence","slug":"response-length-perception-and-sequence","title":"Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline","date":"2023-05-22","arxiv_id":"2305.13144","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-feature-learning-approach-based-on","slug":"a-hybrid-feature-learning-approach-based-on","title":"A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data","date":"2023-05-17","arxiv_id":"2305.10059","repositories_listed":1,"syntology":null},{"url":"/paper/graph-neural-networks-based-user-pairing-in","slug":"graph-neural-networks-based-user-pairing-in","title":"Graph Neural Networks-Based User Pairing in Wireless Communication Systems","date":"2023-05-14","arxiv_id":"2306.00717","repositories_listed":1,"syntology":null},{"url":"/paper/flexible-job-shop-scheduling-via-dual","slug":"flexible-job-shop-scheduling-via-dual","title":"Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning","date":"2023-05-09","arxiv_id":"2305.05119","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-energy-system-scheduling-using-a","slug":"optimal-energy-system-scheduling-using-a","title":"Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm","date":"2023-05-09","arxiv_id":"2305.05484","repositories_listed":1,"syntology":null},{"url":"/paper/data-curation-for-image-captioning-with-text","slug":"data-curation-for-image-captioning-with-text","title":"The Role of Data Curation in Image Captioning","date":"2023-05-05","arxiv_id":"2305.03610","repositories_listed":1,"syntology":null},{"url":"/paper/a-wall-time-minimizing-parallelization","slug":"a-wall-time-minimizing-parallelization","title":"A Wall-time Minimizing Parallelization Strategy for Approximate Bayesian Computation","date":"2023-04-30","arxiv_id":"2305.00506","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-distributional-shifts-in","slug":"addressing-distributional-shifts-in","title":"Addressing distributional shifts in operations management: The case of order fulfillment in customized production","date":"2023-04-24","arxiv_id":"2304.11910","repositories_listed":1,"syntology":null},{"url":"/paper/robust-deep-reinforcement-learning-scheduling","slug":"robust-deep-reinforcement-learning-scheduling","title":"Robust Deep Reinforcement Learning Scheduling via Weight Anchoring","date":"2023-04-20","arxiv_id":"2304.10176","repositories_listed":1,"syntology":null},{"url":"/paper/learning-resource-scheduling-with-high","slug":"learning-resource-scheduling-with-high","title":"Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients","date":"2023-04-19","arxiv_id":"2304.09488","repositories_listed":1,"syntology":null},{"url":"/paper/netgpt-generative-pretrained-transformer-for","slug":"netgpt-generative-pretrained-transformer-for","title":"NetGPT: Generative Pretrained Transformer for Network Traffic","date":"2023-04-19","arxiv_id":"2304.09513","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-model-for-day-ahead-electricity","slug":"a-hybrid-model-for-day-ahead-electricity","title":"A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling","date":"2023-04-18","arxiv_id":"2304.09336","repositories_listed":1,"syntology":null},{"url":"/paper/respect-reinforcement-learning-based-edge","slug":"respect-reinforcement-learning-based-edge","title":"RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs","date":"2023-04-10","arxiv_id":"2304.04716","repositories_listed":1,"syntology":null},{"url":"/paper/fused-depthwise-tiling-for-memory","slug":"fused-depthwise-tiling-for-memory","title":"Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference","date":"2023-03-31","arxiv_id":"2303.17878","repositories_listed":1,"syntology":null},{"url":"/paper/a-graph-neural-network-approach-to","slug":"a-graph-neural-network-approach-to","title":"Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem","date":"2023-03-24","arxiv_id":"2303.13773","repositories_listed":1,"syntology":null},{"url":"/paper/neural-operators-of-backstepping-controller","slug":"neural-operators-of-backstepping-controller","title":"Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs","date":"2023-03-18","arxiv_id":"2303.10506","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-supervised-patchnce-loss-for","slug":"adaptive-supervised-patchnce-loss-for","title":"Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs","date":"2023-03-10","arxiv_id":"2303.06193","repositories_listed":1,"syntology":null},{"url":"/paper/neural-airport-ground-handling","slug":"neural-airport-ground-handling","title":"Neural Airport Ground Handling","date":"2023-03-04","arxiv_id":"2303.02442","repositories_listed":1,"syntology":{"n":10,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/neural-airport-ground-handling#ran","syntology_url":"https://syntology.ai/paper/2303.02442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02442"}},"official":{"repos":["royalskye/agh"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/fedml-parrot-a-scalable-federated-learning","slug":"fedml-parrot-a-scalable-federated-learning","title":"FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training","date":"2023-03-03","arxiv_id":"2303.01778","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-hybrid-spatial-temporal-graph-neural","slug":"adaptive-hybrid-spatial-temporal-graph-neural","title":"Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction","date":"2023-02-28","arxiv_id":"2303.00498","repositories_listed":1,"syntology":null},{"url":"/paper/learning-large-neighborhood-search-for","slug":"learning-large-neighborhood-search-for","title":"Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling","date":"2023-02-27","arxiv_id":"2302.13797","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-for-soft-threshold","slug":"a-unified-framework-for-soft-threshold","title":"A Unified Framework for Soft Threshold Pruning","date":"2023-02-25","arxiv_id":"2302.13019","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/a-unified-framework-for-soft-threshold#ran","syntology_url":"https://syntology.ai/paper/2302.13019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.13019"}},"official":{"repos":["yanqi-chen/lats"],"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/decoupling-the-all-reduce-primitive-for","slug":"decoupling-the-all-reduce-primitive-for","title":"DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining","date":"2023-02-24","arxiv_id":"2302.12445","repositories_listed":1,"syntology":null},{"url":"/paper/decoupled-model-schedule-for-deep-learning","slug":"decoupled-model-schedule-for-deep-learning","title":"Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training","date":"2023-02-16","arxiv_id":"2302.08005","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/decoupled-model-schedule-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2302.08005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08005"}},"official":{"repos":["awslabs/slapo"],"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/guaranteed-dynamic-scheduling-of-ultra","slug":"guaranteed-dynamic-scheduling-of-ultra","title":"Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction","date":"2023-02-15","arxiv_id":"2302.07675","repositories_listed":1,"syntology":null},{"url":"/paper/semiconductor-fab-scheduling-with-self","slug":"semiconductor-fab-scheduling-with-self","title":"Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning","date":"2023-02-14","arxiv_id":"2302.07162","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-simulate-daily-activities-via","slug":"learning-to-simulate-daily-activities-via","title":"Learning to Simulate Daily Activities via Modeling Dynamic Human Needs","date":"2023-02-09","arxiv_id":"2302.10897","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/learning-to-simulate-daily-activities-via#ran","syntology_url":"https://syntology.ai/paper/2302.10897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10897"}},"official":{"repos":["tsinghua-fib-lab/sand"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/map-memory-aware-automated-intra-op-parallel","slug":"map-memory-aware-automated-intra-op-parallel","title":"Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models","date":"2023-02-06","arxiv_id":"2302.02599","repositories_listed":1,"syntology":null},{"url":"/paper/optimization-of-topology-aware-job-allocation","slug":"optimization-of-topology-aware-job-allocation","title":"Optimization of Topology-Aware Job Allocation on a High-Performance Computing Cluster by Neural Simulated Annealing","date":"2023-02-06","arxiv_id":"2302.03517","repositories_listed":1,"syntology":null},{"url":"/paper/learning-coordination-policies-over","slug":"learning-coordination-policies-over","title":"Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation","date":"2023-01-30","arxiv_id":"2301.13279","repositories_listed":1,"syntology":null},{"url":"/paper/victoria-amazonica-optimization-vao-an","slug":"victoria-amazonica-optimization-vao-an","title":"Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant","date":"2023-01-22","arxiv_id":"2303.08070","repositories_listed":1,"syntology":null},{"url":"/paper/flex-net-a-graph-neural-network-approach-to","slug":"flex-net-a-graph-neural-network-approach-to","title":"Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks","date":"2023-01-20","arxiv_id":"2301.11166","repositories_listed":1,"syntology":null},{"url":"/paper/a-fully-adaptive-dro-multistage-framework","slug":"a-fully-adaptive-dro-multistage-framework","title":"A Fully Adaptive DRO Multistage Framework Based on MDR for Generation Scheduling under Uncertainty","date":"2023-01-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/eenet-learning-to-early-exit-for-adaptive","slug":"eenet-learning-to-early-exit-for-adaptive","title":"Adaptive Deep Neural Network Inference Optimization with EENet","date":"2023-01-15","arxiv_id":"2301.07099","repositories_listed":1,"syntology":null},{"url":"/paper/schlably-a-python-framework-for-deep","slug":"schlably-a-python-framework-for-deep","title":"schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments","date":"2023-01-10","arxiv_id":"2301.04182","repositories_listed":1,"syntology":null},{"url":"/paper/ace-a-generic-constraint-solver","slug":"ace-a-generic-constraint-solver","title":"ACE, a generic constraint solver","date":"2023-01-06","arxiv_id":"2302.05405","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-irrigation","slug":"deep-reinforcement-learning-for-irrigation","title":"Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback","date":"2023-01-02","arxiv_id":"2301.00899","repositories_listed":1,"syntology":null},{"url":"/paper/annealing-based-label-transfer-learning-for","slug":"annealing-based-label-transfer-learning-for","title":"Annealing-Based Label-Transfer Learning for Open World Object Detection","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/monge-kantorovich-optimal-transport-through","slug":"monge-kantorovich-optimal-transport-through","title":"Monge-Kantorovich Optimal Transport Through Constrictions and Flow-rate Constraints","date":"2022-12-30","arxiv_id":"2212.14509","repositories_listed":1,"syntology":null},{"url":"/paper/graph-federated-learning-for-ciot-devices-in","slug":"graph-federated-learning-for-ciot-devices-in","title":"Graph Federated Learning for CIoT Devices in Smart Home Applications","date":"2022-12-29","arxiv_id":"2212.14395","repositories_listed":1,"syntology":null},{"url":"/paper/on-realization-of-intelligent-decision-making","slug":"on-realization-of-intelligent-decision-making","title":"On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective","date":"2022-12-24","arxiv_id":"2212.12669","repositories_listed":1,"syntology":null},{"url":"/paper/xengine-optimal-tensor-rematerialization-for","slug":"xengine-optimal-tensor-rematerialization-for","title":"XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments","date":"2022-12-19","arxiv_id":"2212.09290","repositories_listed":1,"syntology":null},{"url":"/paper/dfee-interactive-dataflow-execution-and","slug":"dfee-interactive-dataflow-execution-and","title":"DFEE: Interactive DataFlow Execution and Evaluation Kit","date":"2022-12-04","arxiv_id":"2212.08099","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-stochastic-optimization-with","slug":"end-to-end-stochastic-optimization-with","title":"End-to-End Stochastic Optimization with Energy-Based Model","date":"2022-11-25","arxiv_id":"2211.13837","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/end-to-end-stochastic-optimization-with#ran","syntology_url":"https://syntology.ai/paper/2211.13837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13837"}},"official":{"repos":["Lingkai-Kong/SO-EBM"],"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/learning-to-search-for-job-shop-scheduling","slug":"learning-to-search-for-job-shop-scheduling","title":"Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling","date":"2022-11-20","arxiv_id":"2211.10936","repositories_listed":1,"syntology":null},{"url":"/paper/fedcl-federated-multi-phase-curriculum","slug":"fedcl-federated-multi-phase-curriculum","title":"FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity","date":"2022-11-14","arxiv_id":"2211.07248","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-optimize-permutation-flow-shop","slug":"learning-to-optimize-permutation-flow-shop","title":"Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning","date":"2022-10-31","arxiv_id":"2210.17178","repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-with-prediction-portfolios","slug":"algorithms-with-prediction-portfolios","title":"Algorithms with Prediction Portfolios","date":"2022-10-22","arxiv_id":"2210.12438","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/algorithms-with-prediction-portfolios#ran","syntology_url":"https://syntology.ai/paper/2210.12438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12438"}},"official":{"repos":["tlavastida/predictionportfolios"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-pump-scheduling-problem-a-real-world","slug":"the-pump-scheduling-problem-a-real-world","title":"The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning","date":"2022-10-20","arxiv_id":"2210.11111","repositories_listed":1,"syntology":null},{"url":"/paper/high-dimensional-performance-modeling-via","slug":"high-dimensional-performance-modeling-via","title":"Application Performance Modeling via Tensor Completion","date":"2022-10-18","arxiv_id":"2210.10184","repositories_listed":1,"syntology":null},{"url":"/paper/winner-takes-it-all-training-performant-rl-1","slug":"winner-takes-it-all-training-performant-rl-1","title":"Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization","date":"2022-10-07","arxiv_id":"2210.03475","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/winner-takes-it-all-training-performant-rl-1#ran","syntology_url":"https://syntology.ai/paper/2210.03475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03475"}},"official":null}},{"url":"/paper/neural-network-panning-screening-the-optimal","slug":"neural-network-panning-screening-the-optimal","title":"Neural Network Panning: Screening the Optimal Sparse Network Before Training","date":"2022-09-27","arxiv_id":"2209.13378","repositories_listed":1,"syntology":null},{"url":"/paper/learn-the-time-to-learn-replay-scheduling-in","slug":"learn-the-time-to-learn-replay-scheduling-in","title":"Learn the Time to Learn: Replay Scheduling in Continual Learning","date":"2022-09-18","arxiv_id":"2209.08660","repositories_listed":1,"syntology":null},{"url":"/paper/multiscale-adaptive-scheduling-and-path","slug":"multiscale-adaptive-scheduling-and-path","title":"Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs","date":"2022-09-16","arxiv_id":"2209.07655","repositories_listed":1,"syntology":null},{"url":"/paper/effective-multi-user-delay-constrained","slug":"effective-multi-user-delay-constrained","title":"Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning","date":"2022-08-30","arxiv_id":"2208.14074","repositories_listed":1,"syntology":null},{"url":"/paper/energy-aware-scheduling-of-virtualized-base","slug":"energy-aware-scheduling-of-virtualized-base","title":"Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning","date":"2022-08-21","arxiv_id":"2208.09956","repositories_listed":1,"syntology":null},{"url":"/paper/how-does-data-freshness-affect-real-time","slug":"how-does-data-freshness-affect-real-time","title":"How Does Data Freshness Affect Real-time Supervised Learning?","date":"2022-08-15","arxiv_id":"2208.06948","repositories_listed":1,"syntology":null},{"url":"/paper/segmented-learning-for-class-of-service","slug":"segmented-learning-for-class-of-service","title":"Segmented Learning for Class-of-Service Network Traffic Classification","date":"2022-08-03","arxiv_id":"2208.01793","repositories_listed":1,"syntology":null},{"url":"/paper/off-policy-correction-for-actor-critic","slug":"off-policy-correction-for-actor-critic","title":"Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach","date":"2022-08-01","arxiv_id":"2208.00755","repositories_listed":1,"syntology":null},{"url":"/paper/performance-comparison-of-deep-rl-algorithms","slug":"performance-comparison-of-deep-rl-algorithms","title":"Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling","date":"2022-08-01","arxiv_id":"2208.00728","repositories_listed":1,"syntology":null},{"url":"/paper/supplementing-recurrent-neural-networks-with","slug":"supplementing-recurrent-neural-networks-with","title":"Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems","date":"2022-07-17","arxiv_id":"2207.08189","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/supplementing-recurrent-neural-networks-with#ran","syntology_url":"https://syntology.ai/paper/2207.08189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08189"}},"official":{"repos":["rnn-vca-co/rnn-vca-co"],"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/stochos-stochastic-opportunistic-maintenance","slug":"stochos-stochastic-opportunistic-maintenance","title":"STOCHOS: Stochastic Opportunistic Maintenance Scheduling For Offshore Wind Farms","date":"2022-07-05","arxiv_id":"2207.02274","repositories_listed":1,"syntology":null},{"url":"/paper/a-biased-random-key-genetic-algorithm-for-the","slug":"a-biased-random-key-genetic-algorithm-for-the","title":"A biased random-key genetic algorithm for the home health care problem","date":"2022-06-29","arxiv_id":"2206.14347","repositories_listed":1,"syntology":null},{"url":"/paper/nums-scalable-array-programming-for-the-cloud","slug":"nums-scalable-array-programming-for-the-cloud","title":"NumS: Scalable Array Programming for the Cloud","date":"2022-06-28","arxiv_id":"2206.14276","repositories_listed":1,"syntology":null},{"url":"/paper/epicasting-an-ensemble-wavelet-neural-network","slug":"epicasting-an-ensemble-wavelet-neural-network","title":"Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics","date":"2022-06-21","arxiv_id":"2206.10696","repositories_listed":1,"syntology":null},{"url":"/paper/morphence-2-0-evasion-resilient-moving-target","slug":"morphence-2-0-evasion-resilient-moving-target","title":"Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection","date":"2022-06-15","arxiv_id":"2206.07321","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-and-optimizing-deep-learning","slug":"understanding-and-optimizing-deep-learning","title":"Boosting DNN Cold Inference on Edge Devices","date":"2022-06-15","arxiv_id":"2206.07446","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generalize-dispatching-rules-on","slug":"learning-to-generalize-dispatching-rules-on","title":"Learning to generalize Dispatching rules on the Job Shop Scheduling","date":"2022-06-09","arxiv_id":"2206.04423","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-safe-exploration-using-safety-state","slug":"enhancing-safe-exploration-using-safety-state","title":"Effects of Safety State Augmentation on Safe Exploration","date":"2022-06-06","arxiv_id":"2206.02675","repositories_listed":1,"syntology":null},{"url":"/paper/decentralized-training-of-foundation-models","slug":"decentralized-training-of-foundation-models","title":"Decentralized Training of Foundation Models in Heterogeneous Environments","date":"2022-06-02","arxiv_id":"2206.01288","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/decentralized-training-of-foundation-models#ran","syntology_url":"https://syntology.ai/paper/2206.01288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01288"}},"official":{"repos":["DS3Lab/DT-FM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/felare-fair-scheduling-of-machine-learning","slug":"felare-fair-scheduling-of-machine-learning","title":"FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems","date":"2022-05-31","arxiv_id":"2206.00065","repositories_listed":1,"syntology":null},{"url":"/paper/static-scheduling-with-predictions-learned","slug":"static-scheduling-with-predictions-learned","title":"On Preemption and Learning in Stochastic Scheduling","date":"2022-05-31","arxiv_id":"2205.15695","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":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) · 0 unverified","sample_list":"/paper/static-scheduling-with-predictions-learned#ran","syntology_url":"https://syntology.ai/paper/2205.15695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15695"}},"official":{"repos":["hugorichard/ml4a-scheduling"],"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/autodisc-automatic-distillation-schedule-for","slug":"autodisc-automatic-distillation-schedule-for","title":"MiniDisc: Minimal Distillation Schedule for Language Model Compression","date":"2022-05-29","arxiv_id":"2205.14570","repositories_listed":1,"syntology":null}],"record_sha256":"6e613ea8ceb56ffc46ecaf01d9d47c12697dfefa1799a49e6c55cc75c378b5e8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}