{"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":"/method/speed/papers/73","list_of":"/method/speed","method":"SPEED","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":73,"pages_in_order":96,"rows_per_page":100,"rows":[7201,7300],"of":9573,"counts":{"archive_papers_tagged":9576,"with_a_code_link":3061,"where_syntology_ran_a_sample":779,"not_listed_spam_title":3,"listed":9573,"listed_where_code_ran":779,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":677,"every_run_a_failure_of_syntologys_instrument":102,"listed_with_a_run_with_no_instrument_failure":677,"listed_every_run_a_failure_of_syntologys_instrument":102,"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":"/method/speed","prev":"/method/speed/papers/72","next":"/method/speed/papers/74","papers":[{"paper":null,"slug":"a-comparative-study-of-pretrained-language","title":"A Comparative Study of Pretrained Language Models on Thai Social Text Categorization","date":"2019-12-03","arxiv_id":"1912.01580","n_code_links":0,"syntology":null},{"paper":"/paper/a-deep-learning-based-tool-for-automatic","slug":"a-deep-learning-based-tool-for-automatic","title":"A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents","date":"2019-12-03","arxiv_id":"1912.01359","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-simulation-model-for-pedestrian-crowd","title":"A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques","date":"2019-12-03","arxiv_id":"1912.01629","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-do-urban-incidents-affect-traffic-speed-a","title":"\"How do urban incidents affect traffic speed?\" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction","date":"2019-12-03","arxiv_id":"1912.01242","n_code_links":0,"syntology":null},{"paper":"/paper/pytorch-an-imperative-style-high-performance-1","slug":"pytorch-an-imperative-style-high-performance-1","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","date":"2019-12-03","arxiv_id":"1912.01703","n_code_links":3,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["pytorch/pytorch"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":null,"slug":"emap-explanation-by-minimal-adversarial","title":"EMAP: Explanation by Minimal Adversarial Perturbation","date":"2019-12-02","arxiv_id":"1912.00872","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-model-drift-for-robust-object","title":"Improving Model Drift for Robust Object Tracking","date":"2019-12-02","arxiv_id":"1912.00826","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-model-bootstrapping-using-neural","title":"Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition","date":"2019-12-02","arxiv_id":"1912.00958","n_code_links":0,"syntology":null},{"paper":null,"slug":"risk-bounds-for-low-cost-bipartite-ranking","title":"Risk Bounds for Low Cost Bipartite Ranking","date":"2019-12-02","arxiv_id":"1912.00537","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-auxiliary-task-weighting-for","slug":"adaptive-auxiliary-task-weighting-for","title":"Adaptive Auxiliary Task Weighting for Reinforcement Learning","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/affect-based-intrinsic-rewards-for-learning","slug":"affect-based-intrinsic-rewards-for-learning","title":"Modeling Affect-based Intrinsic Rewards for Exploration and Learning","date":"2019-12-01","arxiv_id":"1912.00403","n_code_links":2,"syntology":null},{"paper":"/paper/behavenet-nonlinear-embedding-and-bayesian","slug":"behavenet-nonlinear-embedding-and-bayesian","title":"BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/deepwave-a-recurrent-neural-network-for-real","slug":"deepwave-a-recurrent-neural-network-for-real","title":"DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging","date":"2019-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/differentiable-cloth-simulation-for-inverse","slug":"differentiable-cloth-simulation-for-inverse","title":"Differentiable Cloth Simulation for Inverse Problems","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-convergence-of-natural-gradient-descent-1","title":"Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/generative-models-for-graph-based-protein","slug":"generative-models-for-graph-based-protein","title":"Generative Models for Graph-Based Protein Design","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-positive-functions-with-pseudo","title":"Learning Positive Functions with Pseudo Mirror Descent","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-stochastic-and-online-learning-with","title":"Optimal Stochastic and Online Learning with Individual Iterates","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-travel-time-estimation-using-matrix","title":"Real-time Travel Time Estimation Using Matrix Factorization","date":"2019-12-01","arxiv_id":"1912.00455","n_code_links":0,"syntology":null},{"paper":null,"slug":"seeing-the-wind-visual-wind-speed-prediction-1","title":"Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/speeding-up-word-movers-distance-and-its","slug":"speeding-up-word-movers-distance-and-its","title":"Speeding up Word Mover's Distance and its variants via properties of distances between embeddings","date":"2019-12-01","arxiv_id":"1912.00509","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["matwerner/fast-wmd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"surround-modulation-a-bio-inspired","title":"Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/temporal-film-capturing-long-range-sequence-1","slug":"temporal-film-capturing-long-range-sequence-1","title":"Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/blockwisely-supervised-neural-architecture","slug":"blockwisely-supervised-neural-architecture","title":"Blockwisely Supervised Neural Architecture Search with Knowledge Distillation","date":"2019-11-29","arxiv_id":"1911.13053","n_code_links":1,"syntology":null},{"paper":"/paper/cagnet-content-aware-guidance-for-salient","slug":"cagnet-content-aware-guidance-for-salient","title":"CAGNet: Content-Aware Guidance for Salient Object Detection","date":"2019-11-29","arxiv_id":"1911.13168","n_code_links":3,"syntology":null},{"paper":null,"slug":"deep-learning-to-scale-up-time-series-traffic","title":"On model selection for scalable time series forecasting in transport networks","date":"2019-11-29","arxiv_id":"1911.13042","n_code_links":0,"syntology":null},{"paper":null,"slug":"induction-of-subgoal-automata-for","title":"Induction of Subgoal Automata for Reinforcement Learning","date":"2019-11-29","arxiv_id":"1911.13152","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatiotemporal-deep-learning-model-for","title":"Spatiotemporal deep learning model for citywide air pollution interpolation and prediction","date":"2019-11-29","arxiv_id":"1911.12919","n_code_links":0,"syntology":null},{"paper":"/paper/stgrat-a-spatio-temporal-graph-attention","slug":"stgrat-a-spatio-temporal-graph-attention","title":"ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed","date":"2019-11-29","arxiv_id":"1911.13181","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-data-driven-approach-to-learning-the","title":"A Data Driven Approach to Learning The Hamiltonian Matrix in Quantum Mechanics","date":"2019-11-28","arxiv_id":"1911.12548","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-network-inspired-analog-to-digital","title":"Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices","date":"2019-11-28","arxiv_id":"1911.12815","n_code_links":0,"syntology":null},{"paper":"/paper/non-autoregressive-video-captioning-with","slug":"non-autoregressive-video-captioning-with","title":"Non-Autoregressive Coarse-to-Fine Video Captioning","date":"2019-11-27","arxiv_id":"1911.12018","n_code_links":1,"syntology":null},{"paper":null,"slug":"qubithd-a-stochastic-acceleration-method-for","title":"QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning","date":"2019-11-27","arxiv_id":"1911.12446","n_code_links":0,"syntology":null},{"paper":"/paper/sapdsoft-anchor-point-detector","slug":"sapdsoft-anchor-point-detector","title":"SAPD：Soft Anchor-Point Detector","date":"2019-11-27","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/soft-anchor-point-object-detection","slug":"soft-anchor-point-object-detection","title":"Soft Anchor-Point Object Detection","date":"2019-11-27","arxiv_id":"1911.12448","n_code_links":2,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"content-based-image-retrieval-speedup","title":"Content-based image retrieval speedup","date":"2019-11-26","arxiv_id":"1911.11379","n_code_links":0,"syntology":null},{"paper":null,"slug":"deeprich-learning-deeply-cherenkov-detectors","title":"DeepRICH: Learning Deeply Cherenkov Detectors","date":"2019-11-26","arxiv_id":"1911.11717","n_code_links":0,"syntology":null},{"paper":null,"slug":"enabling-real-time-multi-messenger","title":"Enabling real-time multi-messenger astrophysics discoveries with deep learning","date":"2019-11-26","arxiv_id":"1911.11779","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-level-network-for-high-speed-multi","title":"Multi-Level Network for High-Speed Multi-Person Pose Estimation","date":"2019-11-26","arxiv_id":"1911.11686","n_code_links":0,"syntology":null},{"paper":null,"slug":"privacy-preserving-neural-network-inference","title":"Privacy preserving Neural Network Inference on Encrypted Data with GPUs","date":"2019-11-26","arxiv_id":"1911.11377","n_code_links":0,"syntology":null},{"paper":"/paper/oops-predicting-unintentional-action-in-video","slug":"oops-predicting-unintentional-action-in-video","title":"Oops! Predicting Unintentional Action in Video","date":"2019-11-25","arxiv_id":"1911.11206","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":null}},{"paper":null,"slug":"real-time-object-tracking-via-meta-learning","title":"Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning","date":"2019-11-25","arxiv_id":"1911.11170","n_code_links":0,"syntology":null},{"paper":null,"slug":"resampling-based-confidence-intervals-for","title":"Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes","date":"2019-11-25","arxiv_id":"1911.11043","n_code_links":0,"syntology":null},{"paper":"/paper/roipca-an-online-pca-algorithm-based-on-rank","slug":"roipca-an-online-pca-algorithm-based-on-rank","title":"ROIPCA: An online memory-restricted PCA algorithm based on rank-one updates","date":"2019-11-25","arxiv_id":"1911.11049","n_code_links":1,"syntology":null},{"paper":"/paper/sub-frame-appearance-and-6d-pose-estimation","slug":"sub-frame-appearance-and-6d-pose-estimation","title":"Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects","date":"2019-11-25","arxiv_id":"1911.10927","n_code_links":2,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["rozumden/deblatting_python"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/traffic-map-prediction-using-unet-based-deep","slug":"traffic-map-prediction-using-unet-based-deep","title":"Traffic map prediction using UNet based deep convolutional neural network","date":"2019-11-25","arxiv_id":"1912.05288","n_code_links":1,"syntology":null},{"paper":"/paper/simple-and-lightweight-human-pose-estimation","slug":"simple-and-lightweight-human-pose-estimation","title":"Simple and Lightweight Human Pose Estimation","date":"2019-11-23","arxiv_id":"1911.10346","n_code_links":1,"syntology":null},{"paper":null,"slug":"computer-vision-based-accident-detection-in","title":"Computer Vision-based Accident Detection in Traffic Surveillance","date":"2019-11-22","arxiv_id":"1911.10037","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-knowledge-aided-explainable-artificial","title":"Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response","date":"2019-11-22","arxiv_id":"1911.09853","n_code_links":0,"syntology":null},{"paper":null,"slug":"implementation-of-optical-deep-neural","title":"Implementation of Optical Deep Neural Networks using the Fabry-Perot Interferometer","date":"2019-11-22","arxiv_id":"1911.10109","n_code_links":0,"syntology":null},{"paper":"/paper/oktoberfest-food-dataset","slug":"oktoberfest-food-dataset","title":"Oktoberfest Food Dataset","date":"2019-11-22","arxiv_id":"1912.05007","n_code_links":1,"syntology":null},{"paper":null,"slug":"parallel-distributed-logistic-regression-for","title":"Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator","date":"2019-11-22","arxiv_id":"1911.09824","n_code_links":0,"syntology":null},{"paper":null,"slug":"schrodinger-ani-an-eight-element-neural","title":"Schrödinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space","date":"2019-11-22","arxiv_id":"1912.05079","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparsetrainleveraging-dynamic-sparsity-in","title":"SparseTrain:Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors","date":"2019-11-22","arxiv_id":"1911.10175","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-graph-transformer-networks-for-brain","title":"Spectral Graph Transformer Networks for Brain Surface Parcellation","date":"2019-11-22","arxiv_id":"1911.10118","n_code_links":0,"syntology":null},{"paper":"/paper/memory-efficient-episodic-control","slug":"memory-efficient-episodic-control","title":"Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means","date":"2019-11-21","arxiv_id":"1911.09560","n_code_links":1,"syntology":null},{"paper":"/paper/cnak-cluster-number-assisted-k-means","slug":"cnak-cluster-number-assisted-k-means","title":"CNAK : Cluster Number Assisted K-means","date":"2019-11-20","arxiv_id":"1911.08871","n_code_links":1,"syntology":null},{"paper":"/paper/discovering-subdimensional-motifs-of","slug":"discovering-subdimensional-motifs-of","title":"Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series","date":"2019-11-20","arxiv_id":"1911.09218","n_code_links":1,"syntology":null},{"paper":null,"slug":"event-based-object-detection-and-tracking-for","title":"Event-based Object Detection and Tracking for Space Situational Awareness","date":"2019-11-20","arxiv_id":"1911.08730","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-non-parametric-learning-to-accelerate","title":"Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control","date":"2019-11-20","arxiv_id":"1911.09214","n_code_links":0,"syntology":null},{"paper":"/paper/fine-tuning-by-curriculum-learning-for-non","slug":"fine-tuning-by-curriculum-learning-for-non","title":"Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation","date":"2019-11-20","arxiv_id":"1911.08717","n_code_links":2,"syntology":{"ran":11,"of":12,"n_ran_checked":11,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["lemmonation/fcl-nat"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/refinedetlite-a-lightweight-one-stage-object","slug":"refinedetlite-a-lightweight-one-stage-object","title":"RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices","date":"2019-11-20","arxiv_id":"1911.08855","n_code_links":1,"syntology":null},{"paper":null,"slug":"titan-a-spatiotemporal-feature-learning","title":"TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction","date":"2019-11-20","arxiv_id":"1911.08684","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimation-of-orientation-and-camera","title":"Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks","date":"2019-11-19","arxiv_id":"1911.08121","n_code_links":0,"syntology":null},{"paper":null,"slug":"inter-layer-collision-networks","title":"IC-Network: Efficient Structure for Convolutional Neural Networks","date":"2019-11-19","arxiv_id":"1911.08252","n_code_links":0,"syntology":null},{"paper":"/paper/neural-network-pruning-with-residual","slug":"neural-network-pruning-with-residual","title":"Neural Network Pruning with Residual-Connections and Limited-Data","date":"2019-11-19","arxiv_id":"1911.08114","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":0,"n_instrument":4,"unverified":3,"pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/parameters-estimation-for-the-cosmic","slug":"parameters-estimation-for-the-cosmic","title":"Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks","date":"2019-11-19","arxiv_id":"1911.08508","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"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","official":{"repos":["JavierOrjuela/BayesianNN_CMB","JavierOrjuela/BayesianNeuralNets_CMB"],"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"]}}},{"paper":null,"slug":"accurate-trajectory-prediction-for-autonomous","title":"Accurate Trajectory Prediction for Autonomous Vehicles","date":"2019-11-18","arxiv_id":"1911.08568","n_code_links":0,"syntology":null},{"paper":null,"slug":"capturing-hand-articulations-using-recurrent","title":"Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations","date":"2019-11-18","arxiv_id":"1911.07424","n_code_links":0,"syntology":null},{"paper":"/paper/influence-aware-memory-for-deep-reinforcement-1","slug":"influence-aware-memory-for-deep-reinforcement-1","title":"Influence-aware Memory Architectures for Deep Reinforcement Learning","date":"2019-11-18","arxiv_id":"1911.07643","n_code_links":1,"syntology":null},{"paper":"/paper/neural-forest-learning","slug":"neural-forest-learning","title":"Neural Random Subspace","date":"2019-11-18","arxiv_id":"1911.07845","n_code_links":1,"syntology":null},{"paper":"/paper/online-adaptive-asymmetric-active-learning","slug":"online-adaptive-asymmetric-active-learning","title":"Online Adaptive Asymmetric Active Learning with Limited Budgets","date":"2019-11-18","arxiv_id":"1911.07498","n_code_links":1,"syntology":null},{"paper":"/paper/any-precision-deep-neural-networks","slug":"any-precision-deep-neural-networks","title":"Any-Precision Deep Neural Networks","date":"2019-11-17","arxiv_id":"1911.07346","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["SHI-Labs/Any-Precision-DNNs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"real-time-semantic-segmentation-via-multiply","title":"Real-Time Semantic Segmentation via Multiply Spatial Fusion Network","date":"2019-11-17","arxiv_id":"1911.07217","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-machine-learning-optimization","title":"Solving machine learning optimization problems using quantum computers","date":"2019-11-17","arxiv_id":"1911.08587","n_code_links":0,"syntology":null},{"paper":null,"slug":"weather-event-severity-prediction-using-buoy","title":"Weather event severity prediction using buoy data and machine learning","date":"2019-11-17","arxiv_id":"1911.09001","n_code_links":0,"syntology":null},{"paper":"/paper/attacut-a-fast-and-accurate-neural-thai-word","slug":"attacut-a-fast-and-accurate-neural-thai-word","title":"AttaCut: A Fast and Accurate Neural Thai Word Segmenter","date":"2019-11-16","arxiv_id":"1911.07056","n_code_links":1,"syntology":null},{"paper":null,"slug":"transfer-learning-of-fmri-dynamics","title":"Transfer Learning of fMRI Dynamics","date":"2019-11-16","arxiv_id":"1911.06813","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-pairwise-comparisons-in-social-choice","title":"Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem","date":"2019-11-14","arxiv_id":"1911.06226","n_code_links":0,"syntology":null},{"paper":null,"slug":"edgenet-balancing-accuracy-and-performance","title":"EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors","date":"2019-11-14","arxiv_id":"1911.06091","n_code_links":0,"syntology":null},{"paper":"/paper/mmwave-radar-point-cloud-segmentation-using","slug":"mmwave-radar-point-cloud-segmentation-using","title":"MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring","date":"2019-11-14","arxiv_id":"1911.06364","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-network-embedding-for-machine-learning-on","title":"On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network","date":"2019-11-14","arxiv_id":"1911.06217","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-clustering-for-mars-rover-image-datasets","title":"Deep Clustering for Mars Rover image datasets","date":"2019-11-12","arxiv_id":"1911.06623","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-networks-gan-based","title":"Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials","date":"2019-11-12","arxiv_id":"1911.05020","n_code_links":0,"syntology":null},{"paper":"/paper/scientific-image-restoration-anywhere","slug":"scientific-image-restoration-anywhere","title":"Scientific Image Restoration Anywhere","date":"2019-11-12","arxiv_id":"1911.05878","n_code_links":2,"syntology":null},{"paper":"/paper/waveletkernelnet-an-interpretable-deep-neural","slug":"waveletkernelnet-an-interpretable-deep-neural","title":"WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis","date":"2019-11-12","arxiv_id":"1911.07925","n_code_links":1,"syntology":null},{"paper":"/paper/gman-a-graph-multi-attention-network-for","slug":"gman-a-graph-multi-attention-network-for","title":"GMAN: A Graph Multi-Attention Network for Traffic Prediction","date":"2019-11-11","arxiv_id":"1911.08415","n_code_links":6,"syntology":{"ran":4,"of":10,"n_ran_checked":4,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["zhengchuanpan/GMAN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/similarity-dt-kernel-similarity-embedding-for","slug":"similarity-dt-kernel-similarity-embedding-for","title":"Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis","date":"2019-11-11","arxiv_id":"1911.04254","n_code_links":1,"syntology":null},{"paper":null,"slug":"191104469","title":"A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking","date":"2019-11-09","arxiv_id":"1911.04469","n_code_links":0,"syntology":null},{"paper":"/paper/centerface-joint-face-detection-and-alignment","slug":"centerface-joint-face-detection-and-alignment","title":"CenterFace: Joint Face Detection and Alignment Using Face as Point","date":"2019-11-09","arxiv_id":"1911.03599","n_code_links":9,"syntology":null},{"paper":null,"slug":"face-detection-in-camera-captured-images-of","title":"Face Detection in Camera Captured Images of Identity Documents under Challenging Conditions","date":"2019-11-08","arxiv_id":"1911.03567","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-pitfall-of-evaluating-performance-on","title":"The Pitfall of Evaluating Performance on Emerging AI Accelerators","date":"2019-11-08","arxiv_id":"1911.02987","n_code_links":0,"syntology":null},{"paper":"/paper/contextualized-sparse-representation-with-1","slug":"contextualized-sparse-representation-with-1","title":"Contextualized Sparse Representations for Real-Time Open-Domain Question Answering","date":"2019-11-07","arxiv_id":"1911.02896","n_code_links":3,"syntology":{"ran":12,"of":18,"n_ran_checked":9,"n_instrument":3,"unverified":6,"pointer_only":7,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 5 honoured, 2 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","official":{"repos":["jhyuklee/sparc"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"graph-domain-adaptation-with-localized-graph","title":"Graph Domain Adaptation with Localized Graph Signal Representations","date":"2019-11-07","arxiv_id":"1911.02883","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-knowledge-distillation-in-non","title":"Understanding Knowledge Distillation in Non-autoregressive Machine Translation","date":"2019-11-07","arxiv_id":"1911.02727","n_code_links":0,"syntology":null},{"paper":"/paper/auptimizer-an-extensible-open-source","slug":"auptimizer-an-extensible-open-source","title":"Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning","date":"2019-11-06","arxiv_id":"1911.02522","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-speed-submission-to-dihard-ii","title":"The Speed Submission to DIHARD II: Contributions & Lessons Learned","date":"2019-11-06","arxiv_id":"1911.02388","n_code_links":0,"syntology":null},{"paper":"/paper/lida-lightweight-interactive-dialogue-1","slug":"lida-lightweight-interactive-dialogue-1","title":"LIDA: Lightweight Interactive Dialogue Annotator","date":"2019-11-05","arxiv_id":"1911.01599","n_code_links":1,"syntology":null},{"paper":null,"slug":"rnn-t-for-latency-controlled-asr-with","title":"RNN-T For Latency Controlled ASR With Improved Beam Search","date":"2019-11-05","arxiv_id":"1911.01629","n_code_links":0,"syntology":null},{"paper":"/paper/fast-optical-system-identification-by","slug":"fast-optical-system-identification-by","title":"Fast Optical System Identification by Numerical Interferometry","date":"2019-11-04","arxiv_id":"1911.01006","n_code_links":1,"syntology":null}],"record_sha256":"10e9970fe42dc5ba6d4aa6ce3be3b7cf6979ab70b09454505ebb3bd69f943740","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}