{"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/computational-efficiency/papers/16","list_of":"/task/computational-efficiency","task":"Computational Efficiency","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":16,"pages_in_order":49,"rows_per_page":100,"rows":[1501,1600],"of":4891,"counts":{"archive_papers_tagged":4891,"with_a_code_link":1644,"where_syntology_ran_a_sample":369,"not_listed_spam_title":0,"listed":4891,"listed_where_code_ran":369,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":307,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":307,"listed_every_run_a_failure_of_syntologys_instrument":62,"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/computational-efficiency","prev":"/task/computational-efficiency/papers/15","next":"/task/computational-efficiency/papers/17","papers":[{"url":"/paper/multilingual-joint-fine-tuning-of-transformer","slug":"multilingual-joint-fine-tuning-of-transformer","title":"Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020","date":"2020-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficientpose-scalable-single-person-pose","slug":"efficientpose-scalable-single-person-pose","title":"EfficientPose: Scalable single-person pose estimation","date":"2020-04-25","arxiv_id":"2004.12186","repositories_listed":1,"syntology":null},{"url":"/paper/miniseg-an-extremely-minimum-network-for","slug":"miniseg-an-extremely-minimum-network-for","title":"MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation","date":"2020-04-21","arxiv_id":"2004.09750","repositories_listed":1,"syntology":null},{"url":"/paper/biophysically-detailed-mathematical-models-of","slug":"biophysically-detailed-mathematical-models-of","title":"Biophysically detailed mathematical models of multiscale cardiac active mechanics","date":"2020-04-16","arxiv_id":"2004.07910","repositories_listed":1,"syntology":null},{"url":"/paper/cortical-surface-registration-using","slug":"cortical-surface-registration-using","title":"Cortical surface registration using unsupervised learning","date":"2020-04-09","arxiv_id":"2004.04617","repositories_listed":1,"syntology":null},{"url":"/paper/direct-loss-minimization-for-sparse-gaussian","slug":"direct-loss-minimization-for-sparse-gaussian","title":"Direct loss minimization algorithms for sparse Gaussian processes","date":"2020-04-07","arxiv_id":"2004.03083","repositories_listed":1,"syntology":null},{"url":"/paper/muxconv-information-multiplexing-in","slug":"muxconv-information-multiplexing-in","title":"MUXConv: Information Multiplexing in Convolutional Neural Networks","date":"2020-03-31","arxiv_id":"2003.13880","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/muxconv-information-multiplexing-in#ran","syntology_url":"https://syntology.ai/paper/2003.13880","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13880"}},"official":{"repos":["human-analysis/MUXConv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-reinforcement-learning-for-large-scale","slug":"deep-reinforcement-learning-for-large-scale","title":"Deep reinforcement learning for large-scale epidemic control","date":"2020-03-30","arxiv_id":"2003.13676","repositories_listed":1,"syntology":null},{"url":"/paper/coping-with-simulators-that-don-t-always","slug":"coping-with-simulators-that-don-t-always","title":"Coping With Simulators That Don't Always Return","date":"2020-03-28","arxiv_id":"2003.12908","repositories_listed":1,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/coping-with-simulators-that-don-t-always#ran","syntology_url":"https://syntology.ai/paper/2003.12908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.12908"}},"official":{"repos":["plai-group/stdr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/gated-texture-cnn-for-efficient-and","slug":"gated-texture-cnn-for-efficient-and","title":"Gated Texture CNN for Efficient and Configurable Image Denoising","date":"2020-03-16","arxiv_id":"2003.07042","repositories_listed":1,"syntology":null},{"url":"/paper/spike-flownet-event-based-optical-flow","slug":"spike-flownet-event-based-optical-flow","title":"Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks","date":"2020-03-14","arxiv_id":"2003.06696","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/spike-flownet-event-based-optical-flow#ran","syntology_url":"https://syntology.ai/paper/2003.06696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06696"}},"official":{"repos":["chan8972/Spike-FlowNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-occlusion-aware-pose-estimation-for","slug":"robust-occlusion-aware-pose-estimation-for","title":"Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands","date":"2020-03-07","arxiv_id":"2003.03518","repositories_listed":1,"syntology":null},{"url":"/paper/learning-directly-from-grammar-compressed","slug":"learning-directly-from-grammar-compressed","title":"Learning Directly from Grammar Compressed Text","date":"2020-02-28","arxiv_id":"2002.12570","repositories_listed":1,"syntology":null},{"url":"/paper/complete-dictionary-learning-via-ell_p-norm","slug":"complete-dictionary-learning-via-ell_p-norm","title":"Complete Dictionary Learning via $\\ell_p$-norm Maximization","date":"2020-02-24","arxiv_id":"2002.10043","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-inexact-bcd-for-coupled-structured","slug":"hybrid-inexact-bcd-for-coupled-structured","title":"Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution","date":"2020-02-20","arxiv_id":"1909.09183","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-for-gaussian-mixture","slug":"a-unified-framework-for-gaussian-mixture","title":"Gaussian Mixture Reduction with Composite Transportation Divergence","date":"2020-02-19","arxiv_id":"2002.08410","repositories_listed":1,"syntology":null},{"url":"/paper/fast-fair-regression-via-efficient","slug":"fast-fair-regression-via-efficient","title":"Fast Fair Regression via Efficient Approximations of Mutual Information","date":"2020-02-14","arxiv_id":"2002.06200","repositories_listed":1,"syntology":null},{"url":"/paper/acenet-anatomical-context-encoding-network","slug":"acenet-anatomical-context-encoding-network","title":"ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation","date":"2020-02-13","arxiv_id":"2002.05773","repositories_listed":1,"syntology":null},{"url":"/paper/sparseids-learning-packet-sampling-with","slug":"sparseids-learning-packet-sampling-with","title":"SparseIDS: Learning Packet Sampling with Reinforcement Learning","date":"2020-02-10","arxiv_id":"2002.03872","repositories_listed":1,"syntology":null},{"url":"/paper/3-d-short-range-imaging-with-irregular-mimo","slug":"3-d-short-range-imaging-with-irregular-mimo","title":"3-D Short-Range Imaging With Irregular {MIMO} Arrays Using {NUFFT-B}ased Range Migration Algorithm","date":"2020-01-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/stream-learn-open-source-python-library-for","slug":"stream-learn-open-source-python-library-for","title":"stream-learn -- open-source Python library for difficult data stream batch analysis","date":"2020-01-29","arxiv_id":"2001.11077","repositories_listed":1,"syntology":null},{"url":"/paper/computing-the-feedback-capacity-of-finite","slug":"computing-the-feedback-capacity-of-finite","title":"Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning","date":"2020-01-27","arxiv_id":"2001.09685","repositories_listed":1,"syntology":null},{"url":"/paper/the-gap-between-theory-and-practice-in","slug":"the-gap-between-theory-and-practice-in","title":"The gap between theory and practice in function approximation with deep neural networks","date":"2020-01-16","arxiv_id":"2001.07523","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/the-gap-between-theory-and-practice-in#ran","syntology_url":"https://syntology.ai/paper/2001.07523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.07523"}},"official":{"repos":["ndexter/MLFA"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/learning-a-neural-3d-texture-space-from-2d","slug":"learning-a-neural-3d-texture-space-from-2d","title":"Learning a Neural 3D Texture Space from 2D Exemplars","date":"2019-12-09","arxiv_id":"1912.04158","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generalization-of-structured-low-rank","slug":"deep-generalization-of-structured-low-rank","title":"Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)","date":"2019-12-07","arxiv_id":"1912.03433","repositories_listed":1,"syntology":null},{"url":"/paper/multi-criterion-evolutionary-design-of-deep","slug":"multi-criterion-evolutionary-design-of-deep","title":"Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification","date":"2019-12-03","arxiv_id":"1912.01369","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-discovery-of-temporal-structure-1","slug":"unsupervised-discovery-of-temporal-structure-1","title":"Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-bootstrap-based-inference-framework-for","slug":"a-bootstrap-based-inference-framework-for","title":"A Bootstrap-based Method for Testing Network Similarity","date":"2019-11-15","arxiv_id":"1911.06869","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-annotation-of-phenotypic","slug":"unsupervised-annotation-of-phenotypic","title":"Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records","date":"2019-11-10","arxiv_id":"1911.03862","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-global-multi-object-tracking-under","slug":"efficient-global-multi-object-tracking-under","title":"Efficient Global Multi-object Tracking Under Minimum-cost Circulation Framework","date":"2019-11-02","arxiv_id":"1911.00796","repositories_listed":1,"syntology":null},{"url":"/paper/recovering-bandits","slug":"recovering-bandits","title":"Recovering Bandits","date":"2019-10-31","arxiv_id":"1910.14354","repositories_listed":1,"syntology":null},{"url":"/paper/191013408","slug":"191013408","title":"A framework for deep learning emulation of numerical models with a case study in satellite remote sensing","date":"2019-10-29","arxiv_id":"1910.13408","repositories_listed":1,"syntology":null},{"url":"/paper/learning-mixtures-of-plackett-luce-models-1","slug":"learning-mixtures-of-plackett-luce-models-1","title":"Learning Mixtures of Plackett-Luce Models from Structured Partial Orders","date":"2019-10-25","arxiv_id":"1910.11721","repositories_listed":1,"syntology":null},{"url":"/paper/prolfa-representative-prototype-selection-for","slug":"prolfa-representative-prototype-selection-for","title":"ProLFA: Representative Prototype Selection for Local Feature Aggregation","date":"2019-10-24","arxiv_id":"1910.11010","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-matching-point","slug":"iterative-matching-point","title":"Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration","date":"2019-10-23","arxiv_id":"1910.10328","repositories_listed":1,"syntology":null},{"url":"/paper/scale-equivariant-steerable-networks-1","slug":"scale-equivariant-steerable-networks-1","title":"Scale-Equivariant Steerable Networks","date":"2019-10-14","arxiv_id":"1910.11093","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/scale-equivariant-steerable-networks-1#ran","syntology_url":"https://syntology.ai/paper/1910.11093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11093"}},"official":null}},{"url":"/paper/exploiting-structural-and-semantic-context","slug":"exploiting-structural-and-semantic-context","title":"Commonsense Knowledge Base Completion with Structural and Semantic Context","date":"2019-10-07","arxiv_id":"1910.02915","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploiting-structural-and-semantic-context#ran","syntology_url":"https://syntology.ai/paper/1910.02915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02915"}},"official":null}},{"url":"/paper/multiagent-rollout-algorithms-and","slug":"multiagent-rollout-algorithms-and","title":"Multiagent Rollout Algorithms and Reinforcement Learning","date":"2019-09-30","arxiv_id":"1910.00120","repositories_listed":1,"syntology":null},{"url":"/paper/additive-powers-of-two-quantization-a-non","slug":"additive-powers-of-two-quantization-a-non","title":"Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks","date":"2019-09-28","arxiv_id":"1909.13144","repositories_listed":1,"syntology":null},{"url":"/paper/graphqa-protein-model-quality-assessment","slug":"graphqa-protein-model-quality-assessment","title":"GraphQA: Protein Model Quality Assessment using Graph Convolutional Network","date":"2019-09-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/augmented-memory-for-correlation-filters-in","slug":"augmented-memory-for-correlation-filters-in","title":"Augmented Memory for Correlation Filters in Real-Time UAV Tracking","date":"2019-09-24","arxiv_id":"1909.10989","repositories_listed":1,"syntology":null},{"url":"/paper/baffle-blockchain-based-aggregator-free","slug":"baffle-blockchain-based-aggregator-free","title":"BAFFLE : Blockchain Based Aggregator Free Federated Learning","date":"2019-09-16","arxiv_id":"1909.07452","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-3d-fully-convolutional-networks-for","slug":"efficient-3d-fully-convolutional-networks-for","title":"Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images","date":"2019-09-16","arxiv_id":"1909.07474","repositories_listed":1,"syntology":null},{"url":"/paper/street-crossing-aid-using-light-weight-cnns","slug":"street-crossing-aid-using-light-weight-cnns","title":"Street Crossing Aid Using Light-weight CNNs for the Visually Impaired","date":"2019-09-14","arxiv_id":"1909.09598","repositories_listed":1,"syntology":null},{"url":"/paper/msstn-multi-scale-spatial-temporal-network","slug":"msstn-multi-scale-spatial-temporal-network","title":"MSSTN: Multi-Scale Spatial Temporal Network for Air Pollution Prediction","date":"2019-09-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/implicit-regularization-for-optimal-sparse","slug":"implicit-regularization-for-optimal-sparse","title":"Implicit Regularization for Optimal Sparse Recovery","date":"2019-09-11","arxiv_id":"1909.05122","repositories_listed":1,"syntology":null},{"url":"/paper/likelihood-free-overcomplete-ica-and","slug":"likelihood-free-overcomplete-ica-and","title":"Likelihood-Free Overcomplete ICA and Applications in Causal Discovery","date":"2019-09-04","arxiv_id":"1909.01525","repositories_listed":1,"syntology":null},{"url":"/paper/flexible-fast-and-accurate-densely-sampled","slug":"flexible-fast-and-accurate-densely-sampled","title":"Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion","date":"2019-08-31","arxiv_id":"1909.01341","repositories_listed":1,"syntology":null},{"url":"/paper/a-nonconvex-approach-for-exact-and-efficient","slug":"a-nonconvex-approach-for-exact-and-efficient","title":"A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution","date":"2019-08-28","arxiv_id":"1908.10776","repositories_listed":1,"syntology":null},{"url":"/paper/point-based-multi-view-stereo-network","slug":"point-based-multi-view-stereo-network","title":"Point-Based Multi-View Stereo Network","date":"2019-08-12","arxiv_id":"1908.04422","repositories_listed":1,"syntology":null},{"url":"/paper/lytnet-a-convolutional-neural-network-for","slug":"lytnet-a-convolutional-neural-network-for","title":"LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired","date":"2019-07-23","arxiv_id":"1907.09706","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-correlation-tracking-via-joint","slug":"real-time-correlation-tracking-via-joint","title":"Real-Time Correlation Tracking via Joint Model Compression and Transfer","date":"2019-07-23","arxiv_id":"1907.09831","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-segmentation-learning-downsampling","slug":"efficient-segmentation-learning-downsampling","title":"Efficient Segmentation: Learning Downsampling Near Semantic Boundaries","date":"2019-07-16","arxiv_id":"1907.07156","repositories_listed":1,"syntology":null},{"url":"/paper/grn-gated-relation-network-to-enhance","slug":"grn-gated-relation-network-to-enhance","title":"GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition","date":"2019-07-12","arxiv_id":"1907.05611","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-inspired-canonical-correlation","slug":"quantum-inspired-canonical-correlation","title":"Quantum-inspired canonical correlation analysis for exponentially large dimensional data","date":"2019-07-07","arxiv_id":"1907.03236","repositories_listed":1,"syntology":null},{"url":"/paper/selection-via-proxy-efficient-data-selection","slug":"selection-via-proxy-efficient-data-selection","title":"Selection via Proxy: Efficient Data Selection for Deep Learning","date":"2019-06-26","arxiv_id":"1906.11829","repositories_listed":1,"syntology":null},{"url":"/paper/first-exit-time-analysis-of-stochastic","slug":"first-exit-time-analysis-of-stochastic","title":"First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise","date":"2019-06-21","arxiv_id":"1906.09069","repositories_listed":1,"syntology":null},{"url":"/paper/trade-offs-in-large-scale-distributed","slug":"trade-offs-in-large-scale-distributed","title":"Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning","date":"2019-06-21","arxiv_id":"1906.09234","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-probabilistic-models-of","slug":"differentiable-probabilistic-models-of","title":"Differentiable probabilistic models of scientific imaging with the Fourier slice theorem","date":"2019-06-18","arxiv_id":"1906.07582","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-of-spatio-temporal","slug":"reinforcement-learning-of-spatio-temporal","title":"Imitation Learning of Neural Spatio-Temporal Point Processes","date":"2019-06-13","arxiv_id":"1906.05467","repositories_listed":1,"syntology":null},{"url":"/paper/famed-net-a-fast-and-accurate-multi-scale-end","slug":"famed-net-a-fast-and-accurate-multi-scale-end","title":"FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network","date":"2019-06-11","arxiv_id":"1906.04334","repositories_listed":1,"syntology":null},{"url":"/paper/learning-gaussian-graphical-models-with-3","slug":"learning-gaussian-graphical-models-with-3","title":"Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization","date":"2019-06-06","arxiv_id":"1906.02719","repositories_listed":1,"syntology":null},{"url":"/paper/gamma-a-general-agent-motion-prediction-model","slug":"gamma-a-general-agent-motion-prediction-model","title":"GAMMA: A General Agent Motion Model for Autonomous Driving","date":"2019-06-04","arxiv_id":"1906.01566","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-discovery-of-temporal-structure","slug":"unsupervised-discovery-of-temporal-structure","title":"Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis","date":"2019-05-23","arxiv_id":"1905.09944","repositories_listed":1,"syntology":null},{"url":"/paper/compression-with-flows-via-local-bits-back","slug":"compression-with-flows-via-local-bits-back","title":"Compression with Flows via Local Bits-Back Coding","date":"2019-05-21","arxiv_id":"1905.08500","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/compression-with-flows-via-local-bits-back#ran","syntology_url":"https://syntology.ai/paper/1905.08500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.08500"}},"official":{"repos":["hojonathanho/localbitsback"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/vitamin-e-visual-tracking-and-mapping-with","slug":"vitamin-e-visual-tracking-and-mapping-with","title":"VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points","date":"2019-04-23","arxiv_id":"1904.10324","repositories_listed":1,"syntology":null},{"url":"/paper/190409408","slug":"190409408","title":"Language Models with Transformers","date":"2019-04-20","arxiv_id":"1904.09408","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/190409408#ran","syntology_url":"https://syntology.ai/paper/1904.09408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09408"}},"official":{"repos":["cgraywang/gluon-nlp-1"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-single-image-dehazing-via-multilevel","slug":"fast-single-image-dehazing-via-multilevel","title":"Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization","date":"2019-04-18","arxiv_id":"1904.08573","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-of-biological-images","slug":"instance-segmentation-of-biological-images","title":"Instance Segmentation of Biological Images Using Harmonic Embeddings","date":"2019-04-10","arxiv_id":"1904.05257","repositories_listed":1,"syntology":null},{"url":"/paper/online-convex-dictionary-learning","slug":"online-convex-dictionary-learning","title":"Online Convex Matrix Factorization with Representative Regions","date":"2019-04-04","arxiv_id":"1904.02580","repositories_listed":1,"syntology":null},{"url":"/paper/preference-neural-network","slug":"preference-neural-network","title":"Preference Neural Network","date":"2019-04-04","arxiv_id":"1904.02345","repositories_listed":1,"syntology":null},{"url":"/paper/easy-transfer-learning-by-exploiting-intra","slug":"easy-transfer-learning-by-exploiting-intra","title":"Easy Transfer Learning By Exploiting Intra-domain Structures","date":"2019-04-02","arxiv_id":"1904.01376","repositories_listed":1,"syntology":null},{"url":"/paper/metric-learning-based-deep-hashing-network","slug":"metric-learning-based-deep-hashing-network","title":"Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images","date":"2019-04-02","arxiv_id":"1904.01258","repositories_listed":1,"syntology":null},{"url":"/paper/representative-datasets-the-perceptron-case","slug":"representative-datasets-the-perceptron-case","title":"Topology-based Representative Datasets to Reduce Neural Network Training Resources","date":"2019-03-20","arxiv_id":"1903.08519","repositories_listed":1,"syntology":null},{"url":"/paper/towards-explainable-ai-significance-tests-for","slug":"towards-explainable-ai-significance-tests-for","title":"Significance Tests for Neural Networks","date":"2019-02-16","arxiv_id":"1902.06021","repositories_listed":1,"syntology":null},{"url":"/paper/iclabel-an-automated-electroencephalographic","slug":"iclabel-an-automated-electroencephalographic","title":"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","date":"2019-01-22","arxiv_id":"1901.07915","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/iclabel-an-automated-electroencephalographic#ran","syntology_url":"https://syntology.ai/paper/1901.07915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07915"}},"official":{"repos":["lucapton/ICLabel-Train"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-learning-exploration-and-mapping-for","slug":"self-learning-exploration-and-mapping-for","title":"Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning","date":"2019-01-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-autoregressive-neural-networks-for-high","slug":"deep-autoregressive-neural-networks-for-high","title":"Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification","date":"2018-12-22","arxiv_id":"1812.09444","repositories_listed":1,"syntology":null},{"url":"/paper/explain-to-fix-a-framework-to-interpret-and","slug":"explain-to-fix-a-framework-to-interpret-and","title":"Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions","date":"2018-11-19","arxiv_id":"1811.08011","repositories_listed":1,"syntology":null},{"url":"/paper/skeleton-based-gesture-recognition-using","slug":"skeleton-based-gesture-recognition-using","title":"Skeleton-based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module","date":"2018-11-17","arxiv_id":"1811.07081","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-the-latent-space-of-light","slug":"deep-learning-the-latent-space-of-light","title":"Deep-learning the Latent Space of Light Transport","date":"2018-11-12","arxiv_id":"1811.04756","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-mst-a-hybrid-active-sampling-strategy","slug":"hybrid-mst-a-hybrid-active-sampling-strategy","title":"Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation","date":"2018-10-20","arxiv_id":"1810.08851","repositories_listed":1,"syntology":null},{"url":"/paper/memc-net-motion-estimation-and-motion","slug":"memc-net-motion-estimation-and-motion","title":"MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement","date":"2018-10-20","arxiv_id":"1810.08768","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/memc-net-motion-estimation-and-motion#ran","syntology_url":"https://syntology.ai/paper/1810.08768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.08768"}},"official":null}},{"url":"/paper/memc-net-motion-estimation-and-motion-1","slug":"memc-net-motion-estimation-and-motion-1","title":"MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement","date":"2018-10-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-structure-learning-by-recursive","slug":"bayesian-structure-learning-by-recursive","title":"Bayesian Structure Learning by Recursive Bootstrap","date":"2018-09-13","arxiv_id":"1809.04828","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-sparse-reconstruction-a-brute-force","slug":"bayesian-sparse-reconstruction-a-brute-force","title":"Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning","date":"2018-09-12","arxiv_id":"1809.04598","repositories_listed":1,"syntology":null},{"url":"/paper/a-minimal-closed-form-solution-for-multi","slug":"a-minimal-closed-form-solution-for-multi","title":"A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines","date":"2018-07-26","arxiv_id":"1807.09970","repositories_listed":1,"syntology":null},{"url":"/paper/road-surface-3d-reconstruction-based-on-dense","slug":"road-surface-3d-reconstruction-based-on-dense","title":"Road surface 3d reconstruction based on dense subpixel disparity map estimation","date":"2018-07-05","arxiv_id":"1807.01874","repositories_listed":1,"syntology":null},{"url":"/paper/deep-convolutional-encoder-decoder-networks","slug":"deep-convolutional-encoder-decoder-networks","title":"Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media","date":"2018-07-02","arxiv_id":"1807.00882","repositories_listed":1,"syntology":null},{"url":"/paper/fast-capsnet-for-lung-cancer-screening","slug":"fast-capsnet-for-lung-cancer-screening","title":"Fast CapsNet for Lung Cancer Screening","date":"2018-06-19","arxiv_id":"1806.07416","repositories_listed":1,"syntology":null},{"url":"/paper/ice-ba-incremental-consistent-and-efficient","slug":"ice-ba-incremental-consistent-and-efficient","title":"ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fast-incremental-von-neumann-graph-entropy","slug":"fast-incremental-von-neumann-graph-entropy","title":"Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications","date":"2018-05-30","arxiv_id":"1805.11769","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-estimation-of-entropy-in-the-fastica","slug":"on-the-estimation-of-entropy-in-the-fastica","title":"On the Estimation of Entropy in the FastICA Algorithm","date":"2018-05-25","arxiv_id":"1805.10206","repositories_listed":1,"syntology":null},{"url":"/paper/parsing-tweets-into-universal-dependencies","slug":"parsing-tweets-into-universal-dependencies","title":"Parsing Tweets into Universal Dependencies","date":"2018-04-23","arxiv_id":"1804.08228","repositories_listed":1,"syntology":null},{"url":"/paper/spatiotemporal-feature-integration-and-model","slug":"spatiotemporal-feature-integration-and-model","title":"SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment","date":"2018-04-13","arxiv_id":"1804.04813","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-extract-a-video-sequence-from-a","slug":"learning-to-extract-a-video-sequence-from-a","title":"Learning to Extract a Video Sequence from a Single Motion-Blurred Image","date":"2018-04-11","arxiv_id":"1804.04065","repositories_listed":1,"syntology":null},{"url":"/paper/finding-influential-training-samples-for","slug":"finding-influential-training-samples-for","title":"Finding Influential Training Samples for Gradient Boosted Decision Trees","date":"2018-02-19","arxiv_id":"1802.06640","repositories_listed":1,"syntology":null},{"url":"/paper/heron-inference-for-bayesian-graphical-models","slug":"heron-inference-for-bayesian-graphical-models","title":"Heron Inference for Bayesian Graphical Models","date":"2018-02-19","arxiv_id":"1802.06526","repositories_listed":1,"syntology":null},{"url":"/paper/hyp-despot-a-hybrid-parallel-algorithm-for","slug":"hyp-despot-a-hybrid-parallel-algorithm-for","title":"HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty","date":"2018-02-17","arxiv_id":"1802.06215","repositories_listed":1,"syntology":null},{"url":"/paper/systematic-weight-pruning-of-dnns-using","slug":"systematic-weight-pruning-of-dnns-using","title":"Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers","date":"2018-02-15","arxiv_id":"1802.05747","repositories_listed":1,"syntology":null}],"record_sha256":"46bb620f80fb823fbc157385092615b7921289a9fbe07314e9f1aed06d0dfc8f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}