{"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/45","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":45,"pages_in_order":49,"rows_per_page":100,"rows":[4401,4500],"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/44","next":"/task/computational-efficiency/papers/46","papers":[{"url":null,"slug":"online-continual-learning-from-imbalanced","title":"Online Continual Learning from Imbalanced Data","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-matching-and-regularized-2","title":"Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-in-video-tracking","title":"Active Learning in Video Tracking","date":"2019-12-29","arxiv_id":"1912.12557","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-plasticity-on-an-accelerated","title":"Structural plasticity on an accelerated analog neuromorphic hardware system","date":"2019-12-27","arxiv_id":"1912.12047","repositories_listed":0,"syntology":null},{"url":null,"slug":"tradi-tracking-deep-neural-network-weight","title":"TRADI: Tracking deep neural network weight distributions for uncertainty estimation","date":"2019-12-24","arxiv_id":"1912.11316","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-polynomial-chaos-expansions-using","title":"Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines","date":"2019-12-23","arxiv_id":"1912.11029","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-approach-to-deep-learning-and","title":"A hierarchical approach to deep learning and its application to tomographic reconstruction","date":"2019-12-16","arxiv_id":"1912.07743","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-posteriori-trading-inspired-model-free-time","title":"A posteriori Trading-inspired Model-free Time Series Segmentation","date":"2019-12-16","arxiv_id":"1912.06708","repositories_listed":0,"syntology":null},{"url":null,"slug":"queueing-analysis-of-gpu-based-inference","title":"Queueing Analysis of GPU-Based Inference Servers with Dynamic Batching: A Closed-Form Characterization","date":"2019-12-13","arxiv_id":"1912.06322","repositories_listed":0,"syntology":null},{"url":null,"slug":"chirrup-a-practical-algorithm-for-unsourced","title":"CHIRRUP: a practical algorithm for unsourced multiple access","date":"2019-12-12","arxiv_id":"1811.00879","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdfn-multi-scale-deep-feature-learning","title":"MDFN: Multi-Scale Deep Feature Learning Network for Object Detection","date":"2019-12-10","arxiv_id":"1912.04514","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-for-fast-and-accurate","title":"Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features","date":"2019-12-09","arxiv_id":"1912.04016","repositories_listed":0,"syntology":null},{"url":null,"slug":"value-of-information-based-arbitration","title":"Value-of-Information based Arbitration between Model-based and Model-free Control","date":"2019-12-08","arxiv_id":"1912.05453","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-positive-functions-with-pseudo","title":"Learning Positive Functions with Pseudo Mirror Descent","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"oracle-efficient-algorithms-for-online-linear","title":"Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"singleshot-a-scalable-tucker-tensor","title":"Singleshot : a scalable Tucker tensor decomposition","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fruit-detection-segmentation-and-3d","title":"Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards","date":"2019-11-28","arxiv_id":"1911.12889","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-retinex-mutually-reinforced","title":"Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement","date":"2019-11-26","arxiv_id":"1911.11323","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-level-neighborhood-interpolation-a","title":"Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy","date":"2019-11-21","arxiv_id":"1911.09307","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-flexible-image-blind-denoising-via","title":"Fast and Flexible Image Blind Denoising via Competition of Experts","date":"2019-11-20","arxiv_id":"1911.08724","repositories_listed":0,"syntology":null},{"url":null,"slug":"witchcraft-efficient-pgd-attacks-with-random","title":"WITCHcraft: Efficient PGD attacks with random step size","date":"2019-11-18","arxiv_id":"1911.07989","repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-pathfinding-based-on-high","title":"Optimizing Cooperative path-finding: A Scalable Multi-Agent RRT* with Dynamic Potential Fields","date":"2019-11-16","arxiv_id":"1911.07840","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-coding-on-cascaded-residuals","title":"Sparse Coding on Cascaded Residuals","date":"2019-11-07","arxiv_id":"1911.02749","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-adaptive-generative-adjustment","title":"Global Adaptive Generative Adjustment","date":"2019-11-02","arxiv_id":"1911.00658","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-octave-convolutions-for-robust","title":"Multi-scale Octave Convolutions for Robust Speech Recognition","date":"2019-10-31","arxiv_id":"1910.14443","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-computing-based-hybrid-solution","title":"Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems","date":"2019-10-29","arxiv_id":"1910.13045","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-expectation-propagation","title":"Conditional Expectation Propagation","date":"2019-10-27","arxiv_id":"1910.12360","repositories_listed":0,"syntology":null},{"url":null,"slug":"landmark-ordinal-embedding","title":"Landmark Ordinal Embedding","date":"2019-10-27","arxiv_id":"1910.12379","repositories_listed":0,"syntology":null},{"url":null,"slug":"lrelu-piece-wise-linear-activation-functions","title":"L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization","date":"2019-10-27","arxiv_id":"1910.12259","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-learning-approach-to-reactive","title":"A Statistical Learning Approach to Reactive Power Control in Distribution Systems","date":"2019-10-25","arxiv_id":"1910.13938","repositories_listed":0,"syntology":null},{"url":null,"slug":"crevnet-conditionally-reversible-video","title":"CrevNet: Conditionally Reversible Video Prediction","date":"2019-10-25","arxiv_id":"1910.11577","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-endcoding-for-neural-network-based","title":"Context-endcoding for neural network based skull stripping in magnetic resonance imaging","date":"2019-10-23","arxiv_id":"1910.10798","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiphase-flow-prediction-with-deep-neural","title":"Multiphase flow prediction with deep neural networks","date":"2019-10-21","arxiv_id":"1910.09657","repositories_listed":0,"syntology":null},{"url":null,"slug":"sdcnet-smoothed-dense-convolution-network-for","title":"SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion","date":"2019-10-18","arxiv_id":"1910.08364","repositories_listed":0,"syntology":null},{"url":null,"slug":"coping-with-simulators-that-dont-always","title":"Coping With Simulators That Don’t Always Return","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-simulations-and-uncertainty","title":"Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation","date":"2019-10-14","arxiv_id":"1910.06121","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-triaging-for-fast-reading","title":"Integrated Triaging for Fast Reading Comprehension","date":"2019-09-28","arxiv_id":"1909.13128","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-of-dynamical-systems-an","title":"Online Learning of Dynamical Systems: An Operator Theoretic Approach","date":"2019-09-27","arxiv_id":"1909.12520","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-symmetric-equilibrium-generative","title":"A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation","date":"2019-09-26","arxiv_id":"1909.11936","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-non-asymptotic-comparison-of-svrg-and-sgd","title":"A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-auto-deferring-policy-for-combinatorial","title":"Deep Auto-Deferring Policy for Combinatorial Optimization","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-value-k-means-clustering","title":"Extreme Value k-means Clustering","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-training-of-deep-neural-networks-for-1","title":"Low Rank Training of Deep Neural Networks for Emerging Memory Technology","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-variational-inference","title":"Meta-Learning for Variational Inference","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-negative-tensor-patch-dictionary","title":"Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications","date":"2019-09-25","arxiv_id":"1910.00993","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-column-measure-and-gradient-free-gradient","title":"The column measure and Gradient-Free Gradient Boosting","date":"2019-09-24","arxiv_id":"1909.10960","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-canonical-correlation-analysis-via","title":"Sparse Canonical Correlation Analysis via Concave Minimization","date":"2019-09-17","arxiv_id":"1909.07947","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-learning-based-convexification-of","title":"Ensemble Learning Based Convex Approximation of Three-Phase Power Flow","date":"2019-09-12","arxiv_id":"1909.05748","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-and-applications-of-quantile","title":"Estimation and Applications of Quantile Regression for Binary Longitudinal Data","date":"2019-09-12","arxiv_id":"1909.05560","repositories_listed":0,"syntology":null},{"url":null,"slug":"mebf-a-fast-and-efficient-boolean-matrix","title":"Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation","date":"2019-09-09","arxiv_id":"1909.03991","repositories_listed":0,"syntology":null},{"url":null,"slug":"road-mapping-in-lidar-images-using-a-joint","title":"Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network","date":"2019-09-07","arxiv_id":"1909.04588","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-and-learning-a-dependency-enhanced","title":"Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch","date":"2019-09-06","arxiv_id":"1909.02955","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-learning-disentangled-representations-for","title":"On Learning Disentangled Representations for Gait Recognition","date":"2019-09-05","arxiv_id":"1909.03051","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-non-commutative-bilinear-model-for","title":"A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs","date":"2019-09-04","arxiv_id":"1909.01567","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-networks-for-selection-of","title":"Deep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing","date":"2019-09-04","arxiv_id":"1909.01868","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-morphological-neural-networks","title":"Deep Morphological Neural Networks","date":"2019-09-04","arxiv_id":"1909.01532","repositories_listed":0,"syntology":null},{"url":"/paper/pisep2-pseudo-image-sequence-evolution-based","slug":"pisep2-pseudo-image-sequence-evolution-based","title":"PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction","date":"2019-09-04","arxiv_id":"1909.01818","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-viewpoint-classification-based-3d","title":"Object Viewpoint Classification Based 3D Bounding Box Estimation for Autonomous Vehicles","date":"2019-09-03","arxiv_id":"1909.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multiple-source-hourglass-deep-network-for","title":"A Multiple Source Hourglass Deep Network for Multi-Focus Image Fusion","date":"2019-08-28","arxiv_id":"1908.10945","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-active-learning-based-gaussian","title":"A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis","date":"2019-08-27","arxiv_id":"1908.10341","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-electronic-systems-for-reservoir","title":"Neuromorphic Electronic Systems for Reservoir Computing","date":"2019-08-26","arxiv_id":"1908.09572","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-tracking-in-an-image-sequence","title":"Region Tracking in an Image Sequence: Preventing Driver Inattention","date":"2019-08-23","arxiv_id":"1908.08914","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-nature-inspired-algorithms","title":"Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics","date":"2019-08-22","arxiv_id":"1908.08563","repositories_listed":0,"syntology":null},{"url":"/paper/190807625","slug":"190807625","title":"Action recognition with spatial-temporal discriminative filter banks","date":"2019-08-20","arxiv_id":"1908.07625","repositories_listed":0,"syntology":null},{"url":null,"slug":"music-transcription-based-on-bayesian-piece","title":"Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions","date":"2019-08-18","arxiv_id":"1908.06969","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cooperative-autoencoder-for-population","title":"A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration","date":"2019-08-16","arxiv_id":"1908.05825","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-spatial-field-reconstruction-with","title":"Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks","date":"2019-08-16","arxiv_id":"1908.05835","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-based-classification","title":"A Machine Learning Based Classification Approach for Power Quality Disturbances Exploiting Higher Order Statistics in the EMD Domain","date":"2019-08-14","arxiv_id":"1904.02836","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-spectral-algorithm-for-mean-estimation","title":"A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates","date":"2019-08-13","arxiv_id":"1908.04468","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-learning-via-online-leverage-score","title":"Continual Learning via Online Leverage Score Sampling","date":"2019-08-01","arxiv_id":"1908.00355","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-theory-of-intentions-for-human","title":"Towards a Theory of Intentions for Human-Robot Collaboration","date":"2019-07-31","arxiv_id":"1907.13275","repositories_listed":0,"syntology":null},{"url":"/paper/reservoir-computing-models-for-patient","slug":"reservoir-computing-models-for-patient","title":"Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices","date":"2019-07-22","arxiv_id":"1907.09504","repositories_listed":0,"syntology":null},{"url":null,"slug":"some-new-results-for-poisson-binomial-models","title":"Some New Results for Poisson Binomial Models","date":"2019-07-21","arxiv_id":"1907.09053","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-matching-and-regularized-1","title":"Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model","date":"2019-07-20","arxiv_id":"1907.08880","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-channel-estimation-for","title":"Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems","date":"2019-07-16","arxiv_id":"1907.07206","repositories_listed":0,"syntology":null},{"url":null,"slug":"more-supervision-less-computation-statistical-1","title":"More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning","date":"2019-07-14","arxiv_id":"1907.06257","repositories_listed":0,"syntology":null},{"url":null,"slug":"gain-with-no-pain-efficient-kernel-pca-by","title":"Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling","date":"2019-07-11","arxiv_id":"1907.05226","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-targeted-acceleration-and-compression","title":"A Targeted Acceleration and Compression Framework for Low bit Neural Networks","date":"2019-07-09","arxiv_id":"1907.05271","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-generate-synthetic-3d-training","title":"Learning to Generate Synthetic 3D Training Data through Hybrid Gradient","date":"2019-06-29","arxiv_id":"1907.00267","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositionally-warped-gaussian-processes","title":"Compositionally-Warped Gaussian Processes","date":"2019-06-23","arxiv_id":"1906.09665","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-control-based-approach-to-batch-process","title":"Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization","date":"2019-06-20","arxiv_id":"1906.08546","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-placement-on-cluttered-surfaces-a","title":"Object Placement on Cluttered Surfaces: A Nested Local Search Approach","date":"2019-06-20","arxiv_id":"1906.08494","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-nonconvex-sdp-solvers-for-large-scale","title":"Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation","date":"2019-06-19","arxiv_id":"1906.07970","repositories_listed":0,"syntology":null},{"url":null,"slug":"warping-resilient-time-series-embeddings","title":"Warping Resilient Scalable Anomaly Detection in Time Series","date":"2019-06-12","arxiv_id":"1906.05205","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-roll-angle-estimation-algorithm","title":"A Robust Roll Angle Estimation Algorithm Based on Gradient Descent","date":"2019-06-05","arxiv_id":"1906.01894","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attentional-models-for-lattice-inputs","title":"Self-Attentional Models for Lattice Inputs","date":"2019-06-04","arxiv_id":"1906.01617","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-shape-reconstruction-from-images-in-the","title":"3D Shape Reconstruction From Images in the Frequency Domain","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-high-dimensional-continuous-time","title":"Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler","date":"2019-05-30","arxiv_id":"1905.13120","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-subroutines-by-watching","title":"Learning Navigation Subroutines from Egocentric Videos","date":"2019-05-29","arxiv_id":"1905.12612","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-chan-vese-model-with-elastica-and","title":"The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation","date":"2019-05-27","arxiv_id":"1905.11192","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-stabilized-explicit-variable-load-solver","title":"The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics","date":"2019-05-21","arxiv_id":"1905.09395","repositories_listed":0,"syntology":null},{"url":null,"slug":"prototypical-bregman-networks","title":"Prototypical Bregman Networks","date":"2019-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-neural-network-channel-execution-for","title":"Dynamic Neural Network Channel Execution for Efficient Training","date":"2019-05-15","arxiv_id":"1905.06435","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamically-expanded-cnn-array-for-video","title":"Dynamically Expanded CNN Array for Video Coding","date":"2019-05-10","arxiv_id":"1905.04326","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-spectrum-occupancy-learning-via","title":"Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks","date":"2019-05-10","arxiv_id":"1905.04392","repositories_listed":0,"syntology":null},{"url":null,"slug":"190503438","title":"Two-stage Best-scored Random Forest for Large-scale Regression","date":"2019-05-09","arxiv_id":"1905.03438","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-model-for-sampling-high","title":"A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks","date":"2019-05-07","arxiv_id":"1905.02898","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfa-scannet-pyramidal-feature-aggregation","title":"PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis","date":"2019-05-03","arxiv_id":"1905.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"ds-vio-robust-and-efficient-stereo-visual","title":"DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF","date":"2019-05-02","arxiv_id":"1905.00684","repositories_listed":0,"syntology":null}],"record_sha256":"103f547034470163e0386be128974d3b66432cbb058dc18570b45cb2eb120e94","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}