{"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/23","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":23,"pages_in_order":49,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/task/computational-efficiency/papers/24","papers":[{"url":null,"slug":"nonlinear-energy-preserving-model-reduction","title":"Nonlinear energy-preserving model reduction with lifting transformations that quadratize the energy","date":"2025-03-04","arxiv_id":"2503.02273","repositories_listed":0,"syntology":null},{"url":null,"slug":"2503-01676","title":"Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control","date":"2025-03-03","arxiv_id":"2503.01676","repositories_listed":0,"syntology":null},{"url":null,"slug":"correcting-mode-proportion-bias-in","title":"Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy","date":"2025-03-03","arxiv_id":"2503.02108","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecg-emotionnet-nested-mixture-of-expert-nmoe","title":"ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition","date":"2025-03-03","arxiv_id":"2503.01750","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-or-powerful-trade-offs-between","title":"Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media","date":"2025-03-03","arxiv_id":"2503.01082","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-multi-hop-reasoning-in-vision","title":"Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling","date":"2025-03-03","arxiv_id":"2503.01754","repositories_listed":0,"syntology":null},{"url":null,"slug":"peo-improving-bi-factorial-preference","title":"PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation","date":"2025-03-03","arxiv_id":"2503.01233","repositories_listed":0,"syntology":null},{"url":null,"slug":"soybean-disease-detection-via-interpretable","title":"Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating MobileNetV2 and GraphSAGE with Cross-Modal Attention","date":"2025-03-03","arxiv_id":"2503.01284","repositories_listed":0,"syntology":null},{"url":null,"slug":"uplink-transmission-design-for-fluid-antenna","title":"Uplink Transmission Design for Fluid Antenna-Enabled Multiuser MIMO Systems with Imperfect CSI","date":"2025-03-03","arxiv_id":"2503.01668","repositories_listed":0,"syntology":null},{"url":null,"slug":"2503-00781","title":"Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks","date":"2025-03-02","arxiv_id":"2503.00781","repositories_listed":0,"syntology":null},{"url":null,"slug":"2503-01009","title":"Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits","date":"2025-03-02","arxiv_id":"2503.01009","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-adaptive-sampling-methods-in","title":"Evaluation of adaptive sampling methods in scenario generation for virtual safety impact assessment of pre-crash safety systems","date":"2025-03-02","arxiv_id":"2503.00815","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimax-optimal-reinforcement-learning-with","title":"Minimax Optimal Reinforcement Learning with Quasi-Optimism","date":"2025-03-02","arxiv_id":"2503.00810","repositories_listed":0,"syntology":null},{"url":null,"slug":"abstract-rendering-computing-all-seen-in","title":"Abstract Rendering: Computing All Seen in Gaussian Splat Scenes","date":"2025-03-01","arxiv_id":"2503.00308","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-attention-fusion-of-mri-and-jacobian","title":"Cross-Attention Fusion of MRI and Jacobian Maps for Alzheimer's Disease Diagnosis","date":"2025-03-01","arxiv_id":"2503.00586","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-parameter-estimation-for","title":"Optimizing Parameter Estimation for Electrochemical Battery Model: A Comparative Analysis of Operating Profiles on Computational Efficiency and Accuracy","date":"2025-03-01","arxiv_id":"2503.00604","repositories_listed":0,"syntology":null},{"url":null,"slug":"qdcnn-quantum-deep-learning-for-enhancing","title":"QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems","date":"2025-03-01","arxiv_id":"2503.01916","repositories_listed":0,"syntology":null},{"url":null,"slug":"computationally-efficient-safe-control-of","title":"Computationally Efficient Safe Control of Linear Systems under Severe Sensor Attacks","date":"2025-02-28","arxiv_id":"2502.20718","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimax-optimal-kernel-two-sample-tests-with","title":"Minimax Optimal Kernel Two-Sample Tests with Random Features","date":"2025-02-28","arxiv_id":"2502.20755","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-with-joint-tokens-and-local","title":"Transformers with Joint Tokens and Local-Global Attention for Efficient Human Pose Estimation","date":"2025-02-28","arxiv_id":"2503.00232","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-deep-learning-techniques-for","title":"Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications","date":"2025-02-27","arxiv_id":"2503.01886","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-approach-for-automatic-2d","title":"Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas","date":"2025-02-27","arxiv_id":"2502.19760","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-energy-flow-analysis-of-integrated","title":"Dynamic Energy Flow Analysis of Integrated Electricity and Gas Systems: A Semi-Analytical Approach","date":"2025-02-27","arxiv_id":"2502.20022","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedmentalcare-towards-privacy-preserving-fine","title":"FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework","date":"2025-02-27","arxiv_id":"2503.05786","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-probability-aggregation-clustering","title":"Graph Probability Aggregation Clustering","date":"2025-02-27","arxiv_id":"2502.19897","repositories_listed":0,"syntology":null},{"url":null,"slug":"kunlunbaize-llm-with-multi-scale-convolution","title":"KunlunBaize: LLM with Multi-Scale Convolution and Multi-Token Prediction Under TransformerX Framework","date":"2025-02-27","arxiv_id":"2503.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"ruranet-an-unsupervised-learning-method-for","title":"RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering","date":"2025-02-27","arxiv_id":"2502.20224","repositories_listed":0,"syntology":null},{"url":null,"slug":"striving-for-faster-and-better-a-one-layer","title":"Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement","date":"2025-02-27","arxiv_id":"2502.19867","repositories_listed":0,"syntology":null},{"url":null,"slug":"amulet-realignment-during-test-time-for","title":"Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs","date":"2025-02-26","arxiv_id":"2502.19148","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-optimized-multimodal-image-enhancement","title":"CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT under Uneven Illumination","date":"2025-02-26","arxiv_id":"2502.19450","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-suitability-of-different","title":"Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation","date":"2025-02-26","arxiv_id":"2502.19515","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-fidelity-multiphysics-modelling-for","title":"High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator","date":"2025-02-26","arxiv_id":"2502.19543","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-approximate-matrix-multiplication-1","title":"Optimal Approximate Matrix Multiplication over Sliding Windows","date":"2025-02-26","arxiv_id":"2502.18830","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-materials-foundation-model-via-hybrid","title":"A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures","date":"2025-02-25","arxiv_id":"2503.05771","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-implementation-of-a-distributed","title":"Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM","date":"2025-02-25","arxiv_id":"2502.17763","repositories_listed":0,"syntology":null},{"url":null,"slug":"invdriver-intra-instance-aware-vectorized","title":"InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer","date":"2025-02-25","arxiv_id":"2502.17949","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-free-and-parallel-computation-for","title":"Memory-Free and Parallel Computation for Quantized Spiking Neural Networks","date":"2025-02-25","arxiv_id":"2503.00040","repositories_listed":0,"syntology":null},{"url":null,"slug":"transported-memory-networks-accelerating","title":"Transported Memory Networks accelerating Computational Fluid Dynamics","date":"2025-02-25","arxiv_id":"2502.18591","repositories_listed":0,"syntology":null},{"url":null,"slug":"tukey-depth-mechanisms-for-practical-private","title":"Tukey Depth Mechanisms for Practical Private Mean Estimation","date":"2025-02-25","arxiv_id":"2502.18698","repositories_listed":0,"syntology":null},{"url":null,"slug":"architecting-digital-twins-for-intelligent","title":"Architecting Digital Twins for Intelligent Transportation Systems","date":"2025-02-24","arxiv_id":"2502.17646","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-backbones-sparsifying-graphs-through","title":"Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning","date":"2025-02-24","arxiv_id":"2502.17713","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-projections-and-natural-sparsity-in","title":"Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis","date":"2025-02-24","arxiv_id":"2502.17061","repositories_listed":0,"syntology":null},{"url":null,"slug":"disc-dynamic-decomposition-improves-llm","title":"DISC: DISC: Dynamic Decomposition Improves LLM Inference Scaling","date":"2025-02-23","arxiv_id":"2502.16706","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-strictly-predefined-time-convergent-and","title":"A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control","date":"2025-02-22","arxiv_id":"2503.01857","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-parallel-tree-search-for-efficient","title":"Dynamic Parallel Tree Search for Efficient LLM Reasoning","date":"2025-02-22","arxiv_id":"2502.16235","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-measurement-enhancement-in-power","title":"Pseudo-Measurement Enhancement in Power Distribution Systems","date":"2025-02-22","arxiv_id":"2502.16188","repositories_listed":0,"syntology":null},{"url":null,"slug":"since-faithfulness-fails-the-performance","title":"Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery","date":"2025-02-22","arxiv_id":"2502.16056","repositories_listed":0,"syntology":null},{"url":null,"slug":"hard-constraint-learning-approaches-with","title":"Hard constraint learning approaches with trainable influence functions for evolutionary equations","date":"2025-02-21","arxiv_id":"2502.17497","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-formulations-for-system","title":"Probabilistic Formulations for System Identification of Linear Dynamics with Bilinear Observation Models","date":"2025-02-21","arxiv_id":"2502.15667","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enhancement-of-jiang-z-et-al-s-compression","title":"An Enhancement of Jiang, Z., et al.s Compression-Based Classification Algorithm Applied to News Article Categorization","date":"2025-02-20","arxiv_id":"2502.14444","repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-fraud-detection-system-based-on","title":"Financial fraud detection system based on improved random forest and gradient boosting machine (GBM)","date":"2025-02-20","arxiv_id":"2502.15822","repositories_listed":0,"syntology":null},{"url":null,"slug":"fundamental-survey-on-neuromorphic-based","title":"Fundamental Survey on Neuromorphic Based Audio Classification","date":"2025-02-20","arxiv_id":"2502.15056","repositories_listed":0,"syntology":null},{"url":null,"slug":"instashap-interpretable-additive-models","title":"InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly","date":"2025-02-20","arxiv_id":"2502.14177","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-small-llms-for-argument-mining-in","title":"Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment","date":"2025-02-20","arxiv_id":"2502.14389","repositories_listed":0,"syntology":null},{"url":null,"slug":"moshi-moshi-a-model-selection-hijacking","title":"Moshi Moshi? A Model Selection Hijacking Adversarial Attack","date":"2025-02-20","arxiv_id":"2502.14586","repositories_listed":0,"syntology":null},{"url":null,"slug":"quad-llm-mltc-large-language-models-ensemble","title":"QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification","date":"2025-02-20","arxiv_id":"2502.14189","repositories_listed":0,"syntology":null},{"url":"/paper/yolov12-a-breakdown-of-the-key-architectural","slug":"yolov12-a-breakdown-of-the-key-architectural","title":"YOLOv12: A Breakdown of the Key Architectural Features","date":"2025-02-20","arxiv_id":"2502.14740","repositories_listed":0,"syntology":null},{"url":null,"slug":"backpropagation-free-spiking-neural-networks","title":"Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm","date":"2025-02-19","arxiv_id":"2502.20411","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficientpose-6d-scalable-and-efficient-6d","title":"EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation","date":"2025-02-19","arxiv_id":"2502.14061","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-non-transitivity-in-llm-as-a","title":"Investigating Non-Transitivity in LLM-as-a-Judge","date":"2025-02-19","arxiv_id":"2502.14074","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-sensitive-security-constrained-economic","title":"Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and Algorithm Design","date":"2025-02-19","arxiv_id":"2502.14150","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-timesteps-a-novel-activation-wise","title":"Activation-wise Propagation: A Universal Strategy to Break Timestep Constraints in Spiking Neural Networks for 3D Data Processing","date":"2025-02-18","arxiv_id":"2502.12791","repositories_listed":0,"syntology":null},{"url":null,"slug":"condensnet-enabling-stable-long-term-climate","title":"CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints","date":"2025-02-18","arxiv_id":"2502.13185","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsmoe-matrix-partitioned-experts-with-dynamic","title":"DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs","date":"2025-02-18","arxiv_id":"2502.12455","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-privacy-utility-and-efficiency","title":"Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models","date":"2025-02-18","arxiv_id":"2502.13313","repositories_listed":0,"syntology":null},{"url":null,"slug":"imove-instance-motion-aware-video","title":"iMOVE: Instance-Motion-Aware Video Understanding","date":"2025-02-17","arxiv_id":"2502.11594","repositories_listed":0,"syntology":null},{"url":null,"slug":"imts-mixer-mixer-networks-for-irregular","title":"IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting","date":"2025-02-17","arxiv_id":"2502.11816","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-free-system-identification-of-surface","title":"Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control","date":"2025-02-17","arxiv_id":"2502.15782","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-query-complexity-of-verifier-assisted","title":"On the Query Complexity of Verifier-Assisted Language Generation","date":"2025-02-17","arxiv_id":"2502.12123","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetric-rank-one-quasi-newton-methods-for","title":"Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization","date":"2025-02-17","arxiv_id":"2502.12298","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-deterministic-diffusion-model","title":"Collaborative Deterministic-Probabilistic Forecasting for Real-World Spatiotemporal Systems","date":"2025-02-16","arxiv_id":"2502.11013","repositories_listed":0,"syntology":null},{"url":null,"slug":"emergent-functions-of-noise-driven","title":"Emergent functions of noise-driven spontaneous activity: Homeostatic maintenance of criticality and memory consolidation","date":"2025-02-16","arxiv_id":"2502.10946","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-topics-in-optimization","title":"Practical Topics in Optimization","date":"2025-02-16","arxiv_id":"2503.05882","repositories_listed":0,"syntology":null},{"url":null,"slug":"remind-remembering-anatomical-variations-for","title":"RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation","date":"2025-02-15","arxiv_id":"2502.10887","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-lexical-manifold-construction","title":"Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation","date":"2025-02-14","arxiv_id":"2502.10013","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-enabled-predictive-control-for-flexible","title":"Data-Enabled Predictive Control for Flexible Spacecraft","date":"2025-02-13","arxiv_id":"2502.09531","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-md3c-taming-masked-diffusion-transformers","title":"E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization","date":"2025-02-13","arxiv_id":"2502.09164","repositories_listed":0,"syntology":null},{"url":null,"slug":"flame-flexible-llm-assisted-moderation-engine","title":"FLAME: Flexible LLM-Assisted Moderation Engine","date":"2025-02-13","arxiv_id":"2502.09175","repositories_listed":0,"syntology":null},{"url":null,"slug":"gora-gradient-driven-adaptive-low-rank","title":"GoRA: Gradient-driven Adaptive Low Rank Adaptation","date":"2025-02-13","arxiv_id":"2502.12171","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-time-user-level-dp-sco-via-robust","title":"Linear-Time User-Level DP-SCO via Robust Statistics","date":"2025-02-13","arxiv_id":"2502.08889","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-range-lidar-vehicle-detection-through","title":"Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing","date":"2025-02-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rolling-ahead-diffusion-for-traffic-scene","title":"Rolling Ahead Diffusion for Traffic Scene Simulation","date":"2025-02-13","arxiv_id":"2502.09587","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-convergence-in-large-language","title":"Structured Convergence in Large Language Model Representations via Hierarchical Latent Space Folding","date":"2025-02-13","arxiv_id":"2502.08947","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-complexity-plug-and-play-deep-learning","title":"A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites","date":"2025-02-12","arxiv_id":"2502.08757","repositories_listed":0,"syntology":null},{"url":null,"slug":"coast-intelligent-time-adaptive-neural","title":"TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion","date":"2025-02-12","arxiv_id":"2502.08574","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-compression-encoding-for-large","title":"Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning","date":"2025-02-12","arxiv_id":"2502.08323","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resolution-neural-networks","title":"Low-Resolution Neural Networks","date":"2025-02-12","arxiv_id":"2502.08795","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-digital-twin-based-controller","title":"Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems Deployment","date":"2025-02-12","arxiv_id":"2502.08115","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-memory-for-online-interdomain","title":"Recurrent Memory for Online Interdomain Gaussian Processes","date":"2025-02-12","arxiv_id":"2502.08736","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-paradox-of-stochasticity-limited","title":"The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data","date":"2025-02-12","arxiv_id":"2502.08515","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-patterns-behind-sports","title":"Exploring Patterns Behind Sports","date":"2025-02-11","arxiv_id":"2502.07491","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-cos-a-fast-one-stage-object-detector","title":"Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving","date":"2025-02-11","arxiv_id":"2502.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedapa-server-side-gradient-based-adaptive","title":"FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data","date":"2025-02-11","arxiv_id":"2502.07456","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-universal-scaling-and-ultra-small","title":"Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity","date":"2025-02-11","arxiv_id":"2502.07293","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-inverse-laplacian-pyramid-for","title":"Learning Inverse Laplacian Pyramid for Progressive Depth Completion","date":"2025-02-11","arxiv_id":"2502.07289","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-term-simulation-of-physical-and","title":"Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks","date":"2025-02-11","arxiv_id":"2502.07325","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-integer-linear-programming-for-active","title":"Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation","date":"2025-02-11","arxiv_id":"2502.07371","repositories_listed":0,"syntology":null},{"url":null,"slug":"mohave-mixture-of-hierarchical-audio-visual","title":"MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition","date":"2025-02-11","arxiv_id":"2502.10447","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-efficient-rlhf-pipeline-a-unified","title":"Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits","date":"2025-02-11","arxiv_id":"2502.07193","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-by-kernel-regression","title":"Bayesian Optimization by Kernel Regression and Density-based Exploration","date":"2025-02-10","arxiv_id":"2502.06178","repositories_listed":0,"syntology":null}],"record_sha256":"64472697c098b39285905960dda1cf54ac7a80cb7c33ca40bad678a5f14a9375","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}