{"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/43","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":43,"pages_in_order":49,"rows_per_page":100,"rows":[4201,4300],"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/42","next":"/task/computational-efficiency/papers/44","papers":[{"url":null,"slug":"decision-machines-interpreting-decision-tree","title":"Decision Machines: Congruent Decision Trees","date":"2021-01-27","arxiv_id":"2101.11347","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-3d-cardiovascular-hemodynamics","title":"Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning","date":"2021-01-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-sparse-polynomial-chaos-expansion","title":"Data-driven sparse polynomial chaos expansion for models with dependent inputs","date":"2021-01-20","arxiv_id":"2101.07997","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-weighted-h2-optimal-model-order","title":"Frequency-weighted H2-optimal model order reduction via oblique projection","date":"2021-01-17","arxiv_id":"2101.06745","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-shape-matching-descriptor-for-real","title":"A novel shape matching descriptor for real-time hand gesture recognition","date":"2021-01-11","arxiv_id":"2101.03923","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolution-based-distillation-for-efficient","title":"Resolution-Based Distillation for Efficient Histology Image Classification","date":"2021-01-11","arxiv_id":"2101.04170","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-and-efficient-framework-for-sports","title":"A Robust and Efficient Framework for Sports-Field Registration","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-unified-information-regularization","title":"A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-training-time-vs-performance-with","title":"Balancing training time vs. performance with Bayesian Early Pruning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-neural-networks-with-variance","title":"Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dct-snn-using-dct-to-distribute-spatial","title":"DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diet-snn-a-low-latency-spiking-neural-network","title":"DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"diffautoml-differentiable-joint-optimization","title":"DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dtmnet-a-discrete-tchebichef-moments-based","title":"DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-number-of-communities-in","title":"Estimation of Number of Communities in Assortative Sparse Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"globally-optimal-and-efficient-manhattan","title":"Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-stochastic-variance-reduced-gradient","title":"On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization","date":"2021-01-01","arxiv_id":"2101.00236","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-over-all-sequences-of-orthogonal","title":"Optimizing Over All Sequences of Orthogonal Polynomials","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-quantized-neural-networks-with","title":"Optimizing Quantized Neural Networks with Natural Gradient","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-convolution-towards-an-optimal","title":"Rethinking Convolution: Towards an Optimal Efficiency","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-compressed-video","title":"Self-Supervised Learning of Compressed Video Representations","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-consistent-saliency-estimation","title":"Statistically Consistent Saliency Estimation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-grained-trajectory-graph-convolutional","title":"Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction","date":"2020-12-23","arxiv_id":"2012.12558","repositories_listed":0,"syntology":null},{"url":null,"slug":"molecular-ct-unifying-geometry-and","title":"Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales","date":"2020-12-22","arxiv_id":"2012.11816","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifying-homophily-and-heterophily-network","title":"Unifying Homophily and Heterophily Network Transformation via Motifs","date":"2020-12-21","arxiv_id":"2012.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterizing-the-evasion-attackability-of","title":"Characterizing the Evasion Attackability of Multi-label Classifiers","date":"2020-12-17","arxiv_id":"2012.09427","repositories_listed":0,"syntology":null},{"url":null,"slug":"mix-a-multi-task-learning-approach-to-solve","title":"MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering","date":"2020-12-17","arxiv_id":"2012.09766","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-transport-for-vector-gaussian-mixture","title":"Optimal transport for vector Gaussian mixture models","date":"2020-12-16","arxiv_id":"2012.09226","repositories_listed":0,"syntology":null},{"url":"/paper/monocular-real-time-full-body-capture-with","slug":"monocular-real-time-full-body-capture-with","title":"Monocular Real-time Full Body Capture with Inter-part Correlations","date":"2020-12-11","arxiv_id":"2012.06087","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-generate-content-aware-dynamic","title":"Learning to Generate Content-Aware Dynamic Detectors","date":"2020-12-08","arxiv_id":"2012.04265","repositories_listed":0,"syntology":null},{"url":null,"slug":"ihashnet-iris-hashing-network-based-on","title":"IHashNet: Iris Hashing Network based on efficient multi-index hashing","date":"2020-12-07","arxiv_id":"2012.03881","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-deep-actor-critic-learning","title":"A Hierarchical Deep Actor-Critic Learning Method for Joint Distribution System State Estimation","date":"2020-12-04","arxiv_id":"2012.02880","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecast-with-forecasts-diversity-matters","title":"Forecast with Forecasts: Diversity Matters","date":"2020-12-03","arxiv_id":"2012.01643","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-based-approach-for-bilevel","title":"Gaussian Process-based Approach for Bilevel Optimization in the Power System -- A Critical Load Restoration Case","date":"2020-12-02","arxiv_id":"2012.01388","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-efficiency-in-hierarchical-reinforcement","title":"On Efficiency in Hierarchical Reinforcement Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fast-and-accurate-neural-chinese-word","title":"Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-assessment-of-electricity","title":"Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method","date":"2020-11-30","arxiv_id":"2011.14937","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-plant-disease-diagonasis-with","title":"Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors","date":"2020-11-29","arxiv_id":"2011.14306","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-triplet-loss-uncertainty","title":"Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval","date":"2020-11-25","arxiv_id":"2011.12663","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-neural-networks-a-survey","title":"Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications","date":"2020-11-25","arxiv_id":"2011.12960","repositories_listed":0,"syntology":null},{"url":null,"slug":"rise-slam-a-resource-aware-inverse-schmidt","title":"RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM","date":"2020-11-23","arxiv_id":"2011.11730","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-text-mining-using-domain","title":"Accelerating Text Mining Using Domain-Specific Stop Word Lists","date":"2020-11-18","arxiv_id":"2012.02294","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-radio-technology-and-modulation","title":"Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder","date":"2020-11-16","arxiv_id":"2011.08295","repositories_listed":0,"syntology":null},{"url":null,"slug":"switching-device-cognizant-sequential","title":"Switching Device-Cognizant Sequential Distribution System Restoration","date":"2020-11-16","arxiv_id":"2011.08236","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-scalable-earth-texture-synthesis","title":"Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks","date":"2020-11-13","arxiv_id":"2011.06776","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-estimation-in-high-dimensional","title":"Support estimation in high-dimensional heteroscedastic mean regression","date":"2020-11-03","arxiv_id":"2011.01591","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-function-analysis-and-implementation","title":"Transfer Function Analysis and Implementation of Active Disturbance Rejection Control","date":"2020-11-02","arxiv_id":"2011.01044","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-iterative-blind-calibration-of-nested","title":"Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting","date":"2020-10-28","arxiv_id":"2010.14799","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-driven-sparse-polynomial-chaos","title":"A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables","date":"2020-10-27","arxiv_id":"2010.14358","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-training-for-monocular-human-mesh","title":"Synthetic Training for Monocular Human Mesh Recovery","date":"2020-10-27","arxiv_id":"2010.14036","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-topic-learning-for-video","title":"Multimodal Topic Learning for Video Recommendation","date":"2020-10-26","arxiv_id":"2010.13373","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-end-to-end-learning-of-cross-event","title":"Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies","date":"2020-10-24","arxiv_id":"2010.12787","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearly-optimal-variational-inference-for-high","title":"Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors","date":"2020-10-24","arxiv_id":"2010.12887","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-modular-framework-for-distributed-model","title":"A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)","date":"2020-10-23","arxiv_id":"2010.12315","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-folding-and-two-channel-filter-banks","title":"Spectral folding and two-channel filter-banks on arbitrary graphs","date":"2020-10-23","arxiv_id":"2010.12604","repositories_listed":0,"syntology":null},{"url":null,"slug":"computationally-and-statistically-efficient","title":"Computationally and Statistically Efficient Truncated Regression","date":"2020-10-22","arxiv_id":"2010.12000","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-atmospheric-temperature","title":"Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface","date":"2020-10-22","arxiv_id":"2010.11557","repositories_listed":0,"syntology":null},{"url":null,"slug":"cimon-towards-high-quality-hash-codes","title":"CIMON: Towards High-quality Hash Codes","date":"2020-10-15","arxiv_id":"2010.07804","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-motion-planning-using-deep","title":"Multi-Agent Motion Planning using Deep Learning for Space Applications","date":"2020-10-15","arxiv_id":"2010.07935","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-deep-factors-for-forecasting","title":"Graph Deep Factors for Forecasting","date":"2020-10-14","arxiv_id":"2010.07373","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-and-computationally-efficient","title":"Sample and Computationally Efficient Stochastic Kriging in High Dimensions","date":"2020-10-14","arxiv_id":"2010.06802","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-important-are-faces-for-person-re","title":"How important are faces for person re-identification?","date":"2020-10-13","arxiv_id":"2010.06307","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-human-performance-on-automatic-motion","title":"Towards human-level performance on automatic pose estimation of infant spontaneous movements","date":"2020-10-12","arxiv_id":"2010.05949","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-truck-platooning-patterns-through","title":"Mining Truck Platooning Patterns Through Massive Trajectory Data","date":"2020-10-11","arxiv_id":"2010.05142","repositories_listed":0,"syntology":null},{"url":null,"slug":"reward-biased-maximum-likelihood-estimation","title":"Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits","date":"2020-10-08","arxiv_id":"2010.04091","repositories_listed":0,"syntology":null},{"url":null,"slug":"swift-scalable-wasserstein-factorization-for","title":"SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors","date":"2020-10-08","arxiv_id":"2010.04081","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-optimal-regret-bounds-for-model-free-rl-1","title":"Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control","date":"2020-10-07","arxiv_id":"2010.03161","repositories_listed":0,"syntology":null},{"url":null,"slug":"episodic-memory-for-learning-subjective-1","title":"Episodic Memory for Learning Subjective-Timescale Models","date":"2020-10-03","arxiv_id":"2010.01430","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computationally-efficient-reconstruction","title":"A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks","date":"2020-10-01","arxiv_id":"2010.00421","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-deep-learning-methods-for","title":"A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time","date":"2020-09-27","arxiv_id":"2009.12942","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-systematic-computational-framework","title":"Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World","date":"2020-09-25","arxiv_id":"2009.12213","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandit-change-point-detection-for-real-time","title":"Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control","date":"2020-09-24","arxiv_id":"2009.11891","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-driven-receding-horizon-control-for","title":"Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems","date":"2020-09-24","arxiv_id":"2009.11958","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-weighted-robust-lda-for-multiclass","title":"Self-Weighted Robust LDA for Multiclass Classification with Edge Classes","date":"2020-09-24","arxiv_id":"2009.12362","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-derivative-free-method-for-quantum","title":"A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks","date":"2020-09-23","arxiv_id":"2009.13264","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-growing-with-convolutional-neural","title":"Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation","date":"2020-09-23","arxiv_id":"2009.11717","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-spiking-sparse-recovery-via-non","title":"Improving Spiking Sparse Recovery via Non-Convex Penalties","date":"2020-09-19","arxiv_id":"2009.09163","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-based-update-of-synapses-in-voltage","title":"Event-based update of synapses in voltage-based learning rules","date":"2020-09-18","arxiv_id":"2009.08667","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-fourier-decomposition-an-accurate","title":"Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method","date":"2020-09-17","arxiv_id":"2009.08047","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-group-learning","title":"Collaborative Group Learning","date":"2020-09-16","arxiv_id":"2009.07712","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-tool-to-study-high-dimensional","title":"Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors","date":"2020-09-16","arxiv_id":"2009.07479","repositories_listed":0,"syntology":null},{"url":null,"slug":"physiologically-valid-3d-0d-closed-loop-model","title":"A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation","date":"2020-09-16","arxiv_id":"2009.08802","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixed-inducing-points-online-bayesian","title":"Fixed Inducing Points Online Bayesian Calibration for Computer Models with an Application to a Scale-Resolving CFD Simulation","date":"2020-09-15","arxiv_id":"2009.07184","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-folded-attention-for-3d-medical","title":"Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation","date":"2020-09-13","arxiv_id":"2009.05576","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-optimal-solution-manifolds-using","title":"Extracting Optimal Solution Manifolds using Constrained Neural Optimization","date":"2020-09-13","arxiv_id":"2009.06024","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-training-of-deep-neural-networks-for","title":"Low-Rank Training of Deep Neural Networks for Emerging Memory Technology","date":"2020-09-08","arxiv_id":"2009.03887","repositories_listed":0,"syntology":null},{"url":null,"slug":"isotonic-regression-with-unknown-permutations","title":"Isotonic regression with unknown permutations: Statistics, computation, and adaptation","date":"2020-09-05","arxiv_id":"2009.02609","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiency-in-real-time-webcam-gaze-tracking","title":"Efficiency in Real-time Webcam Gaze Tracking","date":"2020-09-02","arxiv_id":"2009.01270","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-the-space-complexity-of-planted-clique","title":"Is the space complexity of planted clique recovery the same as that of detection?","date":"2020-08-28","arxiv_id":"2008.12825","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-induced-gaussian-processes-for-large","title":"Locally induced Gaussian processes for large-scale simulation experiments","date":"2020-08-28","arxiv_id":"2008.12857","repositories_listed":0,"syntology":null},{"url":"/paper/neural-bridge-sampling-for-evaluating-safety","slug":"neural-bridge-sampling-for-evaluating-safety","title":"Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems","date":"2020-08-24","arxiv_id":"2008.10581","repositories_listed":0,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/neural-bridge-sampling-for-evaluating-safety#ran","syntology_url":"https://syntology.ai/paper/2008.10581","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.10581"}},"official":null}},{"url":null,"slug":"adversarial-imitation-learning-via-random","title":"Adversarial Imitation Learning via Random Search","date":"2020-08-21","arxiv_id":"2008.09450","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unsupervised-approach-to-ultrasound","title":"An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation","date":"2020-08-21","arxiv_id":"2008.09572","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubly-stochastic-variational-inference-for-2","title":"Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables","date":"2020-08-21","arxiv_id":"2008.09469","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-slic-fuzzy-simple-linear-iterative","title":"Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering","date":"2020-08-21","arxiv_id":"1812.10932","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligence-plays-dice-stochasticity-is","title":"Intelligence plays dice: Stochasticity is essential for machine learning","date":"2020-08-17","arxiv_id":"2008.07496","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-conditional-density-estimation","title":"Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting","date":"2020-08-17","arxiv_id":"2008.07653","repositories_listed":0,"syntology":null},{"url":null,"slug":"principal-ellipsoid-analysis-pea-efficient","title":"Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering","date":"2020-08-17","arxiv_id":"2008.07110","repositories_listed":0,"syntology":null},{"url":null,"slug":"diet-snn-direct-input-encoding-with-leakage","title":"DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks","date":"2020-08-09","arxiv_id":"2008.03658","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardware-accelerator-for-adversarial-attacks","title":"Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks","date":"2020-08-03","arxiv_id":"2008.01219","repositories_listed":0,"syntology":null}],"record_sha256":"fa4bb46e21e00421a25640a7ad6d0ddf83b7e6d0b6186f3fca40f7e1033110f3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}