{"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/44","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":44,"pages_in_order":49,"rows_per_page":100,"rows":[4301,4400],"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/43","next":"/task/computational-efficiency/papers/45","papers":[{"url":null,"slug":"global-and-local-relative-position-embedding","title":"Global-and-Local Relative Position Embedding for Unsupervised Video Summarization","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-signal-processing-for-machine-learning","title":"Graph signal processing for machine learning: A review and new perspectives","date":"2020-07-31","arxiv_id":"2007.16061","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-hierarchical-time-series-using","title":"Prediction of hierarchical time series using structured regularization and its application to artificial neural networks","date":"2020-07-30","arxiv_id":"2007.15159","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergiclearning-neural-network-based-feature","title":"SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning","date":"2020-07-30","arxiv_id":"2007.15222","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-dynamic-inference-with-deep-neural","title":"Fully Dynamic Inference with Deep Neural Networks","date":"2020-07-29","arxiv_id":"2007.15151","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-optimization-of-diversified","title":"Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine","date":"2020-07-28","arxiv_id":"2007.13926","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-allocation-via-model-free-deep","title":"Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications","date":"2020-07-27","arxiv_id":"2007.13709","repositories_listed":0,"syntology":null},{"url":null,"slug":"langevin-monte-carlo-random-coordinate","title":"Langevin Monte Carlo: random coordinate descent and variance reduction","date":"2020-07-26","arxiv_id":"2007.14209","repositories_listed":0,"syntology":null},{"url":null,"slug":"gp-aligner-unsupervised-non-rigid-groupwise","title":"GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor","date":"2020-07-25","arxiv_id":"2007.12979","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-mobility-model-to-support-the-routing","title":"A Novel Mobility Model to Support the Routing of Mobile Energy Resources","date":"2020-07-22","arxiv_id":"2007.11191","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-visual-slam","title":"Privacy Preserving Visual SLAM","date":"2020-07-20","arxiv_id":"2007.10361","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-clustering-of-high-dimensional","title":"Supervised clustering of high dimensional data using regularized mixture modeling","date":"2020-07-19","arxiv_id":"2007.09720","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferred-energy-management-strategies-for","title":"Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition","date":"2020-07-16","arxiv_id":"2007.08337","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximate-xva-for-european-claims","title":"Approximate XVA for European claims","date":"2020-07-15","arxiv_id":"2007.07701","repositories_listed":0,"syntology":null},{"url":null,"slug":"allpass-feedback-delay-networks","title":"Allpass Feedback Delay Networks","date":"2020-07-14","arxiv_id":"2007.07337","repositories_listed":0,"syntology":null},{"url":null,"slug":"vinnas-variational-inference-based-neural","title":"VINNAS: Variational Inference-based Neural Network Architecture Search","date":"2020-07-12","arxiv_id":"2007.06103","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computational-separation-between-private","title":"A Computational Separation between Private Learning and Online Learning","date":"2020-07-11","arxiv_id":"2007.05665","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-variational-inference-1","title":"Meta-Learning Divergences of Variational Inference","date":"2020-07-06","arxiv_id":"2007.02912","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-from-structured-samples-for","title":"Optimization from Structured Samples for Coverage Functions","date":"2020-07-06","arxiv_id":"2007.02738","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-grid-rendering-networks-for-3d-object","title":"Local Grid Rendering Networks for 3D Object Detection in Point Clouds","date":"2020-07-04","arxiv_id":"2007.02099","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-tandem-learning-for-pattern","title":"Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks","date":"2020-07-02","arxiv_id":"2007.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-jpeg-decoding-and-artifacts","title":"End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network","date":"2020-07-01","arxiv_id":"2007.00639","repositories_listed":0,"syntology":null},{"url":null,"slug":"go-wide-then-narrow-efficient-training-of","title":"Go Wide, Then Narrow: Efficient Training of Deep Thin Networks","date":"2020-07-01","arxiv_id":"2007.00811","repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustic-source-localization-with-the-angular","title":"Acoustic Source Localization with the Angular Spectrum Approach in Continuously Stratified Media","date":"2020-06-30","arxiv_id":"2007.01133","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-privacy-of-hierarchical-census","title":"Differential Privacy of Hierarchical Census Data: An Optimization Approach","date":"2020-06-28","arxiv_id":"2006.15673","repositories_listed":0,"syntology":null},{"url":"/paper/localization-uncertainty-estimation-for","slug":"localization-uncertainty-estimation-for","title":"Localization Uncertainty Estimation for Anchor-Free Object Detection","date":"2020-06-28","arxiv_id":"2006.15607","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-autoencoding-of-pde-inverse","title":"Variational Autoencoding of PDE Inverse Problems","date":"2020-06-28","arxiv_id":"2006.15641","repositories_listed":0,"syntology":null},{"url":null,"slug":"draco-co-optimizing-hardware-utilization-and","title":"DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator","date":"2020-06-26","arxiv_id":"2006.15103","repositories_listed":0,"syntology":null},{"url":null,"slug":"coordination-of-oltc-and-smart-inverters-for","title":"Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks","date":"2020-06-24","arxiv_id":"2006.14382","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-relational-memory-network-for","title":"Recurrent Relational Memory Network for Unsupervised Image Captioning","date":"2020-06-24","arxiv_id":"2006.13611","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-deep-reinforcement-learning-based","title":"Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control","date":"2020-06-22","arxiv_id":"2006.12667","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-em-bayesian-meta-learning","title":"Gradient-EM Bayesian Meta-learning","date":"2020-06-21","arxiv_id":"2006.11764","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-convolutional-neural-network-for-rapid","title":"A deep convolutional neural network model for rapid prediction of fluvial flood inundation","date":"2020-06-20","arxiv_id":"2006.11555","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-optimization-with-graph","title":"Neural Architecture Optimization with Graph VAE","date":"2020-06-18","arxiv_id":"2006.10310","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-erm-on-random-subspaces","title":"The Nyström method for convex loss functions","date":"2020-06-17","arxiv_id":"2006.10016","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-extended-integral-unit-commitment","title":"An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing","date":"2020-06-16","arxiv_id":"1910.12994","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-sparse-connectivity-adversarial-robustness","title":"On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron","date":"2020-06-16","arxiv_id":"2006.09510","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-wasserstein-distance-estimation-with","title":"Faster Wasserstein Distance Estimation with the Sinkhorn Divergence","date":"2020-06-15","arxiv_id":"2006.08172","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-sparsity-attacks-on-deep-neural","title":"Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks","date":"2020-06-14","arxiv_id":"2006.08020","repositories_listed":0,"syntology":null},{"url":null,"slug":"gp3-a-sampling-based-analysis-framework-for","title":"GP3: A Sampling-based Analysis Framework for Gaussian Processes","date":"2020-06-14","arxiv_id":"2006.07871","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-maximum-likelihood-estimation-and","title":"Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial","date":"2020-06-12","arxiv_id":"2006.07454","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-the-band-limited-parameterization","title":"Combining the band-limited parameterization and Semi-Lagrangian Runge--Kutta integration for efficient PDE-constrained LDDMM","date":"2020-06-10","arxiv_id":"2006.06823","repositories_listed":0,"syntology":null},{"url":null,"slug":"gap-learning-to-generate-target-conditioned","title":"GAP++: Learning to generate target-conditioned adversarial examples","date":"2020-06-09","arxiv_id":"2006.05097","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-forgetting-for-safe-online-learning","title":"Smart Forgetting for Safe Online Learning with Gaussian Processes","date":"2020-06-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-mixtures-of-plackett-luce-models-3","title":"Learning Mixtures of Random Utility Models with Features from Incomplete Preferences","date":"2020-06-06","arxiv_id":"2006.03869","repositories_listed":0,"syntology":null},{"url":null,"slug":"look-locally-infer-globally-a-generalizable","title":"Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach","date":"2020-06-04","arxiv_id":"2006.02834","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-with-tensor-networks","title":"Anomaly Detection with Tensor Networks","date":"2020-06-03","arxiv_id":"2006.02516","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-uncertainty-in-model","title":"Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models","date":"2020-06-02","arxiv_id":"2006.01983","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-generate-3d-training-data-through","title":"Learning to Generate 3D Training Data Through Hybrid Gradient","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-scenario-construction-for-stochastic","title":"On scenario construction for stochastic shortest path problems in real road networks","date":"2020-06-01","arxiv_id":"2006.00738","repositories_listed":0,"syntology":null},{"url":null,"slug":"instability-computational-efficiency-and","title":"Instability, Computational Efficiency and Statistical Accuracy","date":"2020-05-22","arxiv_id":"2005.11411","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-distributed-subsampling-for-maximum","title":"Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data","date":"2020-05-21","arxiv_id":"2005.10435","repositories_listed":0,"syntology":null},{"url":null,"slug":"mots-multiple-object-tracking-for-general","title":"MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method","date":"2020-05-19","arxiv_id":"2005.09167","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-machine-learning-approach-for","title":"An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure","date":"2020-05-18","arxiv_id":"2005.09747","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-to-text-adaptation-towards-an","title":"Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation","date":"2020-05-17","arxiv_id":"2005.08213","repositories_listed":0,"syntology":null},{"url":null,"slug":"resmonet-a-residual-mobile-based-network-for","title":"Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion","date":"2020-05-15","arxiv_id":"2005.07649","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-perception-model-for-rapid-and","title":"Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement","date":"2020-05-15","arxiv_id":"2005.07343","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-deep-neuroevolution-on","title":"Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems","date":"2020-05-10","arxiv_id":"2005.04536","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-shrinkage-priors-for-large-time","title":"Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods","date":"2020-05-08","arxiv_id":"2005.03906","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathematical-foundations-of-stable-rkhss","title":"Mathematical foundations of stable RKHSs","date":"2020-05-06","arxiv_id":"2005.02971","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-4th-ai-city-challenge","title":"The 4th AI City Challenge","date":"2020-04-30","arxiv_id":"2004.14619","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-exposure-selection-and-fusion-for","title":"Automatic exposure selection and fusion for high-dynamic-range photography via smartphones","date":"2020-04-22","arxiv_id":"2004.10365","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-online-item-choice-behavior-a","title":"Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective","date":"2020-04-18","arxiv_id":"2004.08519","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-bayesian-inference-of-mixed","title":"Scaling Bayesian inference of mixed multinomial logit models to very large datasets","date":"2020-04-11","arxiv_id":"2004.05426","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-permuted-striped-block-model-and-its","title":"Encoder blind combinatorial compressed sensing","date":"2020-04-10","arxiv_id":"2004.05094","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-neural-networks-to-produce","title":"Towards Reusable Network Components by Learning Compatible Representations","date":"2020-04-08","arxiv_id":"2004.03898","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-scale-estimation-methods-using","title":"Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking","date":"2020-04-06","arxiv_id":"2004.02933","repositories_listed":0,"syntology":null},{"url":null,"slug":"genetic-algorithmic-parameter-optimisation-of","title":"Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model","date":"2020-03-30","arxiv_id":"2003.13850","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-target-regression-via-output-space","title":"Multi-target regression via output space quantization","date":"2020-03-22","arxiv_id":"2003.09896","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-guided-divide-and-conquer-for","title":"Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix","date":"2020-03-17","arxiv_id":"2003.07898","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-radiality-constraints-for-distribution","title":"On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems","date":"2020-03-15","arxiv_id":"1912.05185","repositories_listed":0,"syntology":null},{"url":null,"slug":"bihla-fast-and-high-performance-object","title":"A High-Performance Object Proposals based on Horizontal High Frequency Signal","date":"2020-03-13","arxiv_id":"2003.06124","repositories_listed":0,"syntology":null},{"url":null,"slug":"sdvtracker-real-time-multi-sensor-association","title":"SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles","date":"2020-03-09","arxiv_id":"2003.04447","repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-superpixel-clustering-from-patch","title":"Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching","date":"2020-03-09","arxiv_id":"2003.04414","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-and-robust-cumulative","title":"Energy-efficient and Robust Cumulative Training with Net2Net Transformation","date":"2020-03-02","arxiv_id":"2003.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-proto-object-based-dynamic-visual-saliency","title":"A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation","date":"2020-02-27","arxiv_id":"2002.11898","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-ode-with-universal-flows","title":"Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models","date":"2020-02-26","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"txsimmodeling-training-of-deep-neural","title":"TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems","date":"2020-02-25","arxiv_id":"2002.11151","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-stdp-based-visual-feature-learning","title":"Improving STDP-based Visual Feature Learning with Whitening","date":"2020-02-24","arxiv_id":"2002.10177","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-frequency-calibration-for-doa","title":"Multi-frequency calibration for DOA estimation with distributed sensors","date":"2020-02-24","arxiv_id":"2002.11498","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-beetle-antennae-search","title":"Multi-objective beetle antennae search algorithm","date":"2020-02-24","arxiv_id":"2002.10090","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymptotic-marginal-propensity-to-consume","title":"Asymptotic Linearity of Consumption Functions and Computational Efficiency","date":"2020-02-21","arxiv_id":"2002.09108","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-sampling-accuracy-of-stochastic","title":"Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients","date":"2020-02-20","arxiv_id":"2002.08949","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-bootstrap-exploration-for-bandit","title":"Residual Bootstrap Exploration for Bandit Algorithms","date":"2020-02-19","arxiv_id":"2002.08436","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-sketching-methods-for-privacy","title":"Distributed Sketching Methods for Privacy Preserving Regression","date":"2020-02-16","arxiv_id":"2002.06538","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-vehicle-routing-problems-with-soft-time","title":"Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach","date":"2020-02-13","arxiv_id":"2002.05513","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-modeling-and-control","title":"Development of modeling and control strategies for an approximated Gaussian process","date":"2020-02-12","arxiv_id":"2002.05105","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-flat-latent-manifolds-with-vaes","title":"Learning Flat Latent Manifolds with VAEs","date":"2020-02-12","arxiv_id":"2002.04881","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-affordability-of-robustness","title":"Improving the affordability of robustness training for DNNs","date":"2020-02-11","arxiv_id":"2002.04237","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-adaptive-lstm-network-for-real-time","title":"Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation","date":"2020-02-07","arxiv_id":"2002.02598","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-two-layer-feature-selection-method","title":"A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net","date":"2020-01-30","arxiv_id":"2001.11177","repositories_listed":0,"syntology":null},{"url":null,"slug":"safenet-an-assistive-solution-to-assess","title":"SafeNet: An Assistive Solution to Assess Incoming Threats for Premises","date":"2020-01-27","arxiv_id":"2002.04405","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-algorithm-for-high","title":"A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis","date":"2020-01-22","arxiv_id":"2001.07859","repositories_listed":0,"syntology":null},{"url":null,"slug":"curvature-regularized-surface-reconstruction","title":"Curvature Regularized Surface Reconstruction from Point Cloud","date":"2020-01-22","arxiv_id":"2001.07884","repositories_listed":0,"syntology":null},{"url":"/paper/context-aware-cross-attention-for-skeleton","slug":"context-aware-cross-attention-for-skeleton","title":"Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition","date":"2020-01-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adamt-a-stochastic-optimization-with-trend-1","title":"On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods","date":"2020-01-17","arxiv_id":"2001.06130","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-direction-guided-structure-tensor","title":"Adaptive Direction-Guided Structure Tensor Total Variation","date":"2020-01-16","arxiv_id":"2001.05717","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsnet-an-improvement-for-darknet","title":"A lightweight target detection algorithm based on Mobilenet Convolution","date":"2020-01-16","arxiv_id":"2002.03729","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-coordination-of-first-and-last-mode","title":"One-Shot Coordination of First and Last Mode Transportation","date":"2020-01-05","arxiv_id":"2001.01283","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-iteration-complexity-of-hypergradient-1","title":"On the Iteration Complexity of Hypergradient Computations","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"70c1a273268e3536d5604c4334324110127c1cd5a3d400e3ca407b4bd70ef9d5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}