{"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/31","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":31,"pages_in_order":96,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/method/speed/papers/32","papers":[{"paper":"/paper/genformer-a-deep-learning-based-approach-for","slug":"genformer-a-deep-learning-based-approach-for","title":"GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes","date":"2024-02-03","arxiv_id":"2402.02010","n_code_links":1,"syntology":null},{"paper":null,"slug":"glide-with-a-cape-a-low-hassle-method-to","title":"GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding","date":"2024-02-03","arxiv_id":"2402.02082","n_code_links":0,"syntology":null},{"paper":"/paper/multi-level-feature-aggregation-and-recursive","slug":"multi-level-feature-aggregation-and-recursive","title":"Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network","date":"2024-02-03","arxiv_id":"2402.02286","n_code_links":1,"syntology":null},{"paper":"/paper/tsis-a-supplementary-algorithm-to-t-smiles","slug":"tsis-a-supplementary-algorithm-to-t-smiles","title":"Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation","date":"2024-02-03","arxiv_id":"2402.02164","n_code_links":1,"syntology":null},{"paper":null,"slug":"angular-integral-autocorrelation-for-speed","title":"Angular Integral Autocorrelation for Speed Estimation in Shear Wave Elastography","date":"2024-02-02","arxiv_id":"2402.01954","n_code_links":0,"syntology":null},{"paper":"/paper/bayesian-deep-learning-for-remaining-useful","slug":"bayesian-deep-learning-for-remaining-useful","title":"Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent","date":"2024-02-02","arxiv_id":"2402.01098","n_code_links":1,"syntology":null},{"paper":"/paper/decoding-speculative-decoding","slug":"decoding-speculative-decoding","title":"Decoding Speculative Decoding","date":"2024-02-02","arxiv_id":"2402.01528","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["uw-mad-dash/decoding-speculative-decoding"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/deepaat-deep-automated-aerial-triangulation","slug":"deepaat-deep-automated-aerial-triangulation","title":"DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping","date":"2024-02-02","arxiv_id":"2402.01134","n_code_links":1,"syntology":null},{"paper":null,"slug":"dfml-decentralized-federated-mutual-learning","title":"DFML: Decentralized Federated Mutual Learning","date":"2024-02-02","arxiv_id":"2402.01863","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-stochastic-gradient-descent-a","title":"Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence","date":"2024-02-02","arxiv_id":"2402.01515","n_code_links":0,"syntology":null},{"paper":null,"slug":"fedshift-tackling-dual-heterogeneity-problem","title":"FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation","date":"2024-02-02","arxiv_id":"2402.01070","n_code_links":0,"syntology":null},{"paper":"/paper/cf4j-collaborative-filtering-for-java","slug":"cf4j-collaborative-filtering-for-java","title":"CF4J: Collaborative Filtering for Java","date":"2024-02-01","arxiv_id":"2402.01008","n_code_links":1,"syntology":null},{"paper":null,"slug":"eco-driving-under-localization-uncertainty","title":"Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification","date":"2024-02-01","arxiv_id":"2402.01059","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-training-spiking-neural-networks","title":"Parallel Spiking Unit for Efficient Training of Spiking Neural Networks","date":"2024-02-01","arxiv_id":"2402.00449","n_code_links":0,"syntology":null},{"paper":"/paper/getting-the-most-out-of-your-tokenizer-for","slug":"getting-the-most-out-of-your-tokenizer-for","title":"Getting the most out of your tokenizer for pre-training and domain adaptation","date":"2024-02-01","arxiv_id":"2402.01035","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gautierdag/tokenizer-bench"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ghg-emissions-in-the-eu-28-a-multilevel-club","title":"GHG emissions in the EU-28. A multilevel club convergence study of the Emission Trading System and Effort Sharing Decision mechanisms","date":"2024-02-01","arxiv_id":"2402.01784","n_code_links":0,"syntology":null},{"paper":"/paper/superfiltering-weak-to-strong-data-filtering","slug":"superfiltering-weak-to-strong-data-filtering","title":"Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning","date":"2024-02-01","arxiv_id":"2402.00530","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tianyi-lab/superfiltering"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"how-being-inside-or-outside-of-buildings","title":"How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain","date":"2024-01-31","arxiv_id":"2401.17678","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-scale-generative-ai-text-applied-to","title":"Large Scale Generative AI Text Applied to Sports and Music","date":"2024-01-31","arxiv_id":"2402.15514","n_code_links":0,"syntology":null},{"paper":"/paper/longalign-a-recipe-for-long-context-alignment","slug":"longalign-a-recipe-for-long-context-alignment","title":"LongAlign: A Recipe for Long Context Alignment of Large Language Models","date":"2024-01-31","arxiv_id":"2401.18058","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thudm/longalign"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scavenging-hyena-distilling-transformers-into","title":"Scavenging Hyena: Distilling Transformers into Long Convolution Models","date":"2024-01-31","arxiv_id":"2401.17574","n_code_links":0,"syntology":null},{"paper":null,"slug":"tiered-approach-for-rapid-damage","title":"Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach","date":"2024-01-31","arxiv_id":"2401.17759","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-tool-use-with-chain-of-abstraction","title":"Efficient Tool Use with Chain-of-Abstraction Reasoning","date":"2024-01-30","arxiv_id":"2401.17464","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-urban-traffic-safety-an-evaluation","title":"Enhancing Urban Traffic Safety: An Evaluation on Taipei's Neighborhood Traffic Environment Improvement Program","date":"2024-01-30","arxiv_id":"2401.16752","n_code_links":0,"syntology":null},{"paper":null,"slug":"evolvable-agents-a-fine-grained-approach-for","title":"Evolvable Agents, a Fine Grained Approach for Distributed Evolutionary Computing: Walking towards the Peer-to-Peer Computing Frontiers","date":"2024-01-30","arxiv_id":"2401.17224","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-dual-regularized-autoencoder-for-sparse","title":"Fast Dual-Regularized Autoencoder for Sparse Biological Data","date":"2024-01-30","arxiv_id":"2401.16664","n_code_links":0,"syntology":null},{"paper":"/paper/finetuning-large-language-models-for","slug":"finetuning-large-language-models-for","title":"Finetuning Large Language Models for Vulnerability Detection","date":"2024-01-30","arxiv_id":"2401.17010","n_code_links":1,"syntology":null},{"paper":"/paper/owsm-v3-1-better-and-faster-open-whisper","slug":"owsm-v3-1-better-and-faster-open-whisper","title":"OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer","date":"2024-01-30","arxiv_id":"2401.16658","n_code_links":1,"syntology":null},{"paper":null,"slug":"platoon-fundamental-diagram-estimation-can-be","title":"Platoon Fundamental Diagram estimation can be Markovian: evidence from human- and self-driven vehicle trajectories","date":"2024-01-30","arxiv_id":"2401.17065","n_code_links":0,"syntology":null},{"paper":"/paper/proactive-detection-of-voice-cloning-with","slug":"proactive-detection-of-voice-cloning-with","title":"Proactive Detection of Voice Cloning with Localized Watermarking","date":"2024-01-30","arxiv_id":"2401.17264","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 1 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["facebookresearch/audioseal"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"strokenuwa-tokenizing-strokes-for-vector","title":"StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis","date":"2024-01-30","arxiv_id":"2401.17093","n_code_links":0,"syntology":null},{"paper":"/paper/a-concise-but-effective-network-for-image","slug":"a-concise-but-effective-network-for-image","title":"A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving","date":"2024-01-29","arxiv_id":"2401.15902","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-time-varying-shockwave-speed-model-for","title":"A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations","date":"2024-01-29","arxiv_id":"2402.00063","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-for-voltage","title":"Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information","date":"2024-01-29","arxiv_id":"2401.15848","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-anything-in-medical-images-a","title":"Depth Anything in Medical Images: A Comparative Study","date":"2024-01-29","arxiv_id":"2401.16600","n_code_links":0,"syntology":null},{"paper":null,"slug":"divide-and-conquer-rethinking-the-training","title":"Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields","date":"2024-01-29","arxiv_id":"2401.16144","n_code_links":0,"syntology":null},{"paper":null,"slug":"mosquiot-a-system-based-on-iot-and-machine","title":"MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)","date":"2024-01-29","arxiv_id":"2401.16258","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompting-diverse-ideas-increasing-ai-idea","title":"Prompting Diverse Ideas: Increasing AI Idea Variance","date":"2024-01-27","arxiv_id":"2402.01727","n_code_links":0,"syntology":null},{"paper":null,"slug":"social-interpretable-reinforcement-learning","title":"Social Interpretable Reinforcement Learning","date":"2024-01-27","arxiv_id":"2401.15480","n_code_links":0,"syntology":null},{"paper":"/paper/wind-speed-super-resolution-and-validation","slug":"wind-speed-super-resolution-and-validation","title":"Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models","date":"2024-01-27","arxiv_id":"2401.15469","n_code_links":1,"syntology":null},{"paper":null,"slug":"digital-analog-hybrid-matrix-multiplication","title":"Digital-analog hybrid matrix multiplication processor for optical neural networks","date":"2024-01-26","arxiv_id":"2401.15061","n_code_links":0,"syntology":null},{"paper":null,"slug":"recognizing-multiple-ingredients-in-food","title":"Recognizing Multiple Ingredients in Food Images Using a Single-Ingredient Classification Model","date":"2024-01-26","arxiv_id":"2401.14579","n_code_links":0,"syntology":null},{"paper":"/paper/sketch-and-refine-towards-fast-and-accurate","slug":"sketch-and-refine-towards-fast-and-accurate","title":"Sketch and Refine: Towards Fast and Accurate Lane Detection","date":"2024-01-26","arxiv_id":"2401.14729","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":4,"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) · 0 unverified","official":{"repos":["passerer/srlane"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"super-efficient-neural-network-for","title":"Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution","date":"2024-01-26","arxiv_id":"2401.14641","n_code_links":0,"syntology":null},{"paper":null,"slug":"unit-dsr-dysarthric-speech-reconstruction","title":"UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization","date":"2024-01-26","arxiv_id":"2401.14664","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-zero-shot-inference","title":"A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification","date":"2024-01-25","arxiv_id":"2401.13887","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-real-time-rendering-method-for-high-albedo","title":"A real-time rendering method for high albedo anisotropic materials with multiple scattering","date":"2024-01-25","arxiv_id":"2401.14051","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-stripe-artefact-removal-by-a","title":"Effective stripe artefact removal by a variational method: application to light-sheet microscopy, FIB-SEM and remote sensing images","date":"2024-01-25","arxiv_id":"2401.14220","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-robust-generalizable-radiance-field","title":"Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation","date":"2024-01-25","arxiv_id":"2401.14354","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-application-of-relief-algorithm-in","title":"Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India","date":"2024-01-25","arxiv_id":"2401.14065","n_code_links":0,"syntology":null},{"paper":"/paper/vivim-a-video-vision-mamba-for-medical-video","slug":"vivim-a-video-vision-mamba-for-medical-video","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","date":"2024-01-25","arxiv_id":"2401.14168","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":6,"n_instrument":4,"unverified":3,"pointer_only":13,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","official":{"repos":["scott-yjyang/vivim"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"an-automated-real-time-approach-for-image","title":"An Automated Real-Time Approach for Image Processing and Segmentation of Fluoroscopic Images and Videos Using a Single Deep Learning Network","date":"2024-01-23","arxiv_id":"2401.12488","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-algorithm-for-spatial-spectral","title":"An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI","date":"2024-01-23","arxiv_id":"2401.12890","n_code_links":0,"syntology":null},{"paper":null,"slug":"binary-structured-physics-informed-neural","title":"Binary structured physics-informed neural networks for solving equations with rapidly changing solutions","date":"2024-01-23","arxiv_id":"2401.12806","n_code_links":0,"syntology":null},{"paper":"/paper/correlation-embedded-transformer-tracking-a","slug":"correlation-embedded-transformer-tracking-a","title":"Correlation-Embedded Transformer Tracking: A Single-Branch Framework","date":"2024-01-23","arxiv_id":"2401.12743","n_code_links":1,"syntology":null},{"paper":"/paper/endogaussian-gaussian-splatting-for","slug":"endogaussian-gaussian-splatting-for","title":"EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction","date":"2024-01-23","arxiv_id":"2401.12561","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-implicit-neural-representation-image","title":"An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding","date":"2024-01-23","arxiv_id":"2401.12587","n_code_links":0,"syntology":null},{"paper":null,"slug":"faster-projected-gan-towards-faster-few-shot","title":"Faster Projected GAN: Towards Faster Few-Shot Image Generation","date":"2024-01-23","arxiv_id":"2403.08778","n_code_links":0,"syntology":null},{"paper":"/paper/psavatar-a-point-based-morphable-shape-model","slug":"psavatar-a-point-based-morphable-shape-model","title":"PSAvatar: A Point-based Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting","date":"2024-01-23","arxiv_id":"2401.12900","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-genetic-algorithms-performance-via","title":"Improving genetic algorithms performance via deterministic population shrinkage","date":"2024-01-22","arxiv_id":"2401.12121","n_code_links":0,"syntology":null},{"paper":"/paper/low-tubal-rank-tensor-recovery-via-factorized","slug":"low-tubal-rank-tensor-recovery-via-factorized","title":"Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent","date":"2024-01-22","arxiv_id":"2401.11940","n_code_links":1,"syntology":null},{"paper":"/paper/observation-guided-meteorological-field","slug":"observation-guided-meteorological-field","title":"Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method","date":"2024-01-22","arxiv_id":"2401.11960","n_code_links":1,"syntology":null},{"paper":null,"slug":"back-stepping-experience-replay-with","title":"Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot","date":"2024-01-21","arxiv_id":"2401.11372","n_code_links":0,"syntology":null},{"paper":null,"slug":"differential-privacy-in-hierarchical","title":"Differentially-Private Multi-Tier Federated Learning","date":"2024-01-21","arxiv_id":"2401.11592","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-inspired-chaotic-salp-swarm","title":"Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization","date":"2024-01-21","arxiv_id":"2402.16863","n_code_links":0,"syntology":null},{"paper":"/paper/reframing-offline-reinforcement-learning-as-a","slug":"reframing-offline-reinforcement-learning-as-a","title":"Solving Offline Reinforcement Learning with Decision Tree Regression","date":"2024-01-21","arxiv_id":"2401.11630","n_code_links":1,"syntology":null},{"paper":null,"slug":"tempo-confidentiality-preservation-in-cloud","title":"Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training","date":"2024-01-21","arxiv_id":"2401.11531","n_code_links":0,"syntology":null},{"paper":null,"slug":"equivariant-multiscale-learned-invertible","title":"Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT","date":"2024-01-20","arxiv_id":"2401.11256","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconfigurable-intelligent-surface-enabled-9","title":"Reconfigurable Intelligent Surface-Enabled Array Radar for Interference Mitigation","date":"2024-01-20","arxiv_id":"2401.11137","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-lightweight-fpga-based-ids-ecu-architecture","title":"A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN","date":"2024-01-19","arxiv_id":"2401.12234","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-simple-framework-to-accelerate-multilingual","title":"Accelerating Multilingual Language Model for Excessively Tokenized Languages","date":"2024-01-19","arxiv_id":"2401.10660","n_code_links":0,"syntology":null},{"paper":null,"slug":"autochunk-automated-activation-chunk-for","title":"AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference","date":"2024-01-19","arxiv_id":"2401.10652","n_code_links":0,"syntology":null},{"paper":null,"slug":"helmholtz-decomposition-and-optical-flow-a","title":"Helmholtz-Decomposition and Optical Flow: A new method to characterize GCamP recordings","date":"2024-01-19","arxiv_id":"2401.11008","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-backdoors-for-mixed-integer-programs","title":"Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning","date":"2024-01-19","arxiv_id":"2401.10467","n_code_links":0,"syntology":null},{"paper":"/paper/an-optimization-based-equilibrium-measure","slug":"an-optimization-based-equilibrium-measure","title":"An optimization-based equilibrium measure describes non-equilibrium steady state dynamics: application to edge of chaos","date":"2024-01-18","arxiv_id":"2401.10009","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-dimensionality-reduction-of-twin-in","title":"Automatic dimensionality reduction of Twin-in-the-Loop Observers","date":"2024-01-18","arxiv_id":"2401.10945","n_code_links":0,"syntology":null},{"paper":null,"slug":"eclectic-rule-extraction-for-explainability","title":"Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems","date":"2024-01-18","arxiv_id":"2401.10207","n_code_links":0,"syntology":null},{"paper":"/paper/fregrad-lightweight-and-fast-frequency-aware","slug":"fregrad-lightweight-and-fast-frequency-aware","title":"FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder","date":"2024-01-18","arxiv_id":"2401.10032","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["kaistmm/fregrad"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/hierarchical-federated-learning-in-multi-hop","slug":"hierarchical-federated-learning-in-multi-hop","title":"Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs","date":"2024-01-18","arxiv_id":"2401.10361","n_code_links":1,"syntology":null},{"paper":"/paper/mamba-multi-level-aggregation-via-memory-bank","slug":"mamba-multi-level-aggregation-via-memory-bank","title":"MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection","date":"2024-01-18","arxiv_id":"2401.09923","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["guanxiongsun/vfe.pytorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mobility-accelerates-learning-convergence","title":"Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks","date":"2024-01-18","arxiv_id":"2401.09656","n_code_links":0,"syntology":null},{"paper":null,"slug":"power-system-fault-diagnosis-with-quantum","title":"Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition","date":"2024-01-18","arxiv_id":"2401.09800","n_code_links":0,"syntology":null},{"paper":"/paper/symbolnet-neural-symbolic-regression-with","slug":"symbolnet-neural-symbolic-regression-with","title":"SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression","date":"2024-01-18","arxiv_id":"2401.09949","n_code_links":1,"syntology":null},{"paper":null,"slug":"traffic-smoothing-controllers-for-autonomous","title":"Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data","date":"2024-01-18","arxiv_id":"2401.09666","n_code_links":0,"syntology":null},{"paper":null,"slug":"comb-reference-signal-pattern-design-for","title":"Staggered Comb Reference Signal Design for Integrated Communication and Sensing","date":"2024-01-17","arxiv_id":"2401.09648","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalized-reference-signals-design-for","title":"OFDM Reference Signal Pattern Design Criteria for Integrated Communication and Sensing","date":"2024-01-17","arxiv_id":"2401.09643","n_code_links":0,"syntology":null},{"paper":null,"slug":"monitoring-machine-learning-forecasts-for","title":"Monitoring Machine Learning Forecasts for Platform Data Streams","date":"2024-01-17","arxiv_id":"2401.09144","n_code_links":0,"syntology":null},{"paper":null,"slug":"partial-diacritization-a-context-contrastive","title":"A Context-Contrastive Inference Approach To Partial Diacritization","date":"2024-01-17","arxiv_id":"2401.08919","n_code_links":0,"syntology":null},{"paper":"/paper/rigid-protein-protein-docking-via-equivariant","slug":"rigid-protein-protein-docking-via-equivariant","title":"Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction","date":"2024-01-17","arxiv_id":"2401.08986","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yaledeus/ellidock"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"risk-aware-accelerated-wireless-federated","title":"Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients","date":"2024-01-17","arxiv_id":"2401.09267","n_code_links":0,"syntology":null},{"paper":"/paper/cross-modal-semi-dense-6-dof-tracking-of-an","slug":"cross-modal-semi-dense-6-dof-tracking-of-an","title":"Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions","date":"2024-01-16","arxiv_id":"2401.08043","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-wind-speed-and-wind-power","title":"Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness","date":"2024-01-16","arxiv_id":"2401.08233","n_code_links":0,"syntology":null},{"paper":"/paper/fast-dynamic-3d-object-generation-from-a","slug":"fast-dynamic-3d-object-generation-from-a","title":"Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video","date":"2024-01-16","arxiv_id":"2401.08742","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["fudan-zvg/efficient4d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/faster-isnet-for-background-bias-mitigation","slug":"faster-isnet-for-background-bias-mitigation","title":"Faster ISNet for Background Bias Mitigation on Deep Neural Networks","date":"2024-01-16","arxiv_id":"2401.08409","n_code_links":1,"syntology":null},{"paper":"/paper/hardware-acceleration-for-real-time-wildfire","slug":"hardware-acceleration-for-real-time-wildfire","title":"Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks","date":"2024-01-16","arxiv_id":"2401.08105","n_code_links":1,"syntology":null},{"paper":"/paper/inferflow-an-efficient-and-highly","slug":"inferflow-an-efficient-and-highly","title":"Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models","date":"2024-01-16","arxiv_id":"2401.08294","n_code_links":1,"syntology":null},{"paper":null,"slug":"specstg-a-fast-spectral-diffusion-framework","title":"SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting","date":"2024-01-16","arxiv_id":"2401.08119","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-efficient-and-certified-recovery-from","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","date":"2024-01-16","arxiv_id":"2401.08216","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-aware-calibration-of-a-hot-wire","title":"Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression","date":"2024-01-16","arxiv_id":"2401.09492","n_code_links":0,"syntology":null},{"paper":null,"slug":"carspeednet-a-deep-neural-network-based-car","title":"CarSpeedNet: A Deep Neural Network-based Car Speed Estimation from Smartphone Accelerometer","date":"2024-01-15","arxiv_id":"2401.07468","n_code_links":0,"syntology":null}],"record_sha256":"4c8b407ae0678328cd83e290f4afaf371fdcb8918fd352577f02ee2339901759","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}