{"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/base/papers/31","list_of":"/method/base","method":"BASE","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":58,"rows_per_page":100,"rows":[3001,3100],"of":5784,"counts":{"archive_papers_tagged":5784,"with_a_code_link":1913,"where_syntology_ran_a_sample":621,"not_listed_spam_title":0,"listed":5784,"listed_where_code_ran":621,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":523,"every_run_a_failure_of_syntologys_instrument":98,"listed_with_a_run_with_no_instrument_failure":523,"listed_every_run_a_failure_of_syntologys_instrument":98,"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/base","prev":"/method/base/papers/30","next":"/method/base/papers/32","papers":[{"paper":"/paper/instruction-makes-a-difference","slug":"instruction-makes-a-difference","title":"Instruction Makes a Difference","date":"2024-02-01","arxiv_id":"2402.00453","n_code_links":1,"syntology":null},{"paper":null,"slug":"radio-map-assisted-approach-for-interference","title":"Radio Map Assisted Approach for Interference-Aware Predictive UAV Communications","date":"2024-02-01","arxiv_id":"2402.00569","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectrally-transformed-kernel-regression","title":"Spectrally Transformed Kernel Regression","date":"2024-02-01","arxiv_id":"2402.00645","n_code_links":0,"syntology":null},{"paper":"/paper/vision-mae-a-foundation-model-for-medical","slug":"vision-mae-a-foundation-model-for-medical","title":"VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification","date":"2024-02-01","arxiv_id":"2402.01034","n_code_links":1,"syntology":null},{"paper":"/paper/continuous-unsupervised-domain-adaptation","slug":"continuous-unsupervised-domain-adaptation","title":"Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay","date":"2024-01-31","arxiv_id":"2402.00580","n_code_links":1,"syntology":null},{"paper":"/paper/deductive-beam-search-decoding-deducible","slug":"deductive-beam-search-decoding-deducible","title":"Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning","date":"2024-01-31","arxiv_id":"2401.17686","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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":["osu-nlp-group/deductive-beam-search"],"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":"graph-attention-based-reinforcement-learning","title":"Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication","date":"2024-01-31","arxiv_id":"2401.17880","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-a-long-story-short-in-conversation","title":"Making a Long Story Short in Conversation Modeling","date":"2024-01-31","arxiv_id":"2402.00143","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrosynthesis-prediction-enhanced-by-in","title":"Retrosynthesis prediction enhanced by in-silico reaction data augmentation","date":"2024-01-31","arxiv_id":"2402.00086","n_code_links":0,"syntology":null},{"paper":"/paper/crud-rag-a-comprehensive-chinese-benchmark","slug":"crud-rag-a-comprehensive-chinese-benchmark","title":"CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models","date":"2024-01-30","arxiv_id":"2401.17043","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["iaar-shanghai/crud_rag"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fine-tuning-transformer-based-encoder-for","title":"Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks","date":"2024-01-30","arxiv_id":"2401.17396","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-spike-snn-single-spike-phase-coding-with","title":"One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization","date":"2024-01-30","arxiv_id":"2403.08786","n_code_links":0,"syntology":null},{"paper":null,"slug":"pace-a-pragmatic-agent-for-enhancing","title":"PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models","date":"2024-01-30","arxiv_id":"2402.01750","n_code_links":0,"syntology":null},{"paper":null,"slug":"weaver-foundation-models-for-creative-writing","title":"Weaver: Foundation Models for Creative Writing","date":"2024-01-30","arxiv_id":"2401.17268","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-channel-estimation-for-6","title":"Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System","date":"2024-01-29","arxiv_id":"2402.09439","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-channel-estimation-for-irs","title":"Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System","date":"2024-01-29","arxiv_id":"2402.09441","n_code_links":0,"syntology":null},{"paper":null,"slug":"extreme-learning-machine-based-channel","title":"Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System","date":"2024-01-29","arxiv_id":"2402.09440","n_code_links":0,"syntology":null},{"paper":"/paper/few-and-fewer-learning-better-from-few","slug":"few-and-fewer-learning-better-from-few","title":"Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes","date":"2024-01-29","arxiv_id":"2401.15834","n_code_links":1,"syntology":null},{"paper":"/paper/machine-translation-meta-evaluation-through","slug":"machine-translation-meta-evaluation-through","title":"Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets","date":"2024-01-29","arxiv_id":"2401.16313","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-bd-symbiotic-radio-aided-6g-iot-network","title":"Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach","date":"2024-01-29","arxiv_id":"2401.16085","n_code_links":0,"syntology":null},{"paper":"/paper/textual-entailment-for-effective-triple","slug":"textual-entailment-for-effective-triple","title":"Textual Entailment for Effective Triple Validation in Object Prediction","date":"2024-01-29","arxiv_id":"2401.16293","n_code_links":1,"syntology":null},{"paper":"/paper/data-free-generalized-zero-shot-learning","slug":"data-free-generalized-zero-shot-learning","title":"Data-Free Generalized Zero-Shot Learning","date":"2024-01-28","arxiv_id":"2401.15657","n_code_links":1,"syntology":{"ran":9,"of":14,"n_ran_checked":4,"n_instrument":5,"unverified":5,"pointer_only":14,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","official":{"repos":["ylong4/dfzsl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dynamic-transformer-architecture-for","title":"Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks","date":"2024-01-27","arxiv_id":"2401.15275","n_code_links":0,"syntology":null},{"paper":null,"slug":"equipping-language-models-with-tool-use","title":"Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance","date":"2024-01-27","arxiv_id":"2401.15328","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-and-diagnosing-knowledge-base","slug":"pre-training-and-diagnosing-knowledge-base","title":"Pre-training and Diagnosing Knowledge Base Completion Models","date":"2024-01-27","arxiv_id":"2401.15439","n_code_links":1,"syntology":null},{"paper":"/paper/privacy-preserving-cross-domain-sequential","slug":"privacy-preserving-cross-domain-sequential","title":"Privacy-Preserving Cross-Domain Sequential Recommendation","date":"2024-01-27","arxiv_id":"2401.15369","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-the-secrecy-rate-with-direction","title":"Enhancing the Secrecy Rate with Direction-range Focusing with FDA and RIS","date":"2024-01-26","arxiv_id":"2401.15154","n_code_links":0,"syntology":null},{"paper":null,"slug":"geodecoder-empowering-multimodal-map","title":"GeoDecoder: Empowering Multimodal Map Understanding","date":"2024-01-26","arxiv_id":"2401.15118","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-generalization-capacity-of-neural","slug":"on-the-generalization-capacity-of-neural","title":"On the generalization capacity of neural networks during generic multimodal reasoning","date":"2024-01-26","arxiv_id":"2401.15030","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["ibm/gcog"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"accelerating-retrieval-augmented-language","title":"Accelerating Retrieval-Augmented Language Model Serving with Speculation","date":"2024-01-25","arxiv_id":"2401.14021","n_code_links":0,"syntology":null},{"paper":"/paper/cross-domain-few-shot-learning-via-adaptive","slug":"cross-domain-few-shot-learning-via-adaptive","title":"Cross-Domain Few-Shot Learning via Adaptive Transformer Networks","date":"2024-01-25","arxiv_id":"2401.13987","n_code_links":1,"syntology":null},{"paper":null,"slug":"design-and-implementation-of-hardware","title":"Design and Implementation of Hardware Accelerators for Neural Processing Applications","date":"2024-01-25","arxiv_id":"2402.00051","n_code_links":0,"syntology":null},{"paper":null,"slug":"hi-core-hierarchical-knowledge-transfer-for","title":"Hierarchical Continual Reinforcement Learning via Large Language Model","date":"2024-01-25","arxiv_id":"2401.15098","n_code_links":0,"syntology":null},{"paper":null,"slug":"styleinject-parameter-efficient-tuning-of","title":"StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models","date":"2024-01-25","arxiv_id":"2401.13942","n_code_links":0,"syntology":null},{"paper":"/paper/clue-guided-path-exploration-an-efficient","slug":"clue-guided-path-exploration-an-efficient","title":"Fine-Grained Stateful Knowledge Exploration: A Novel Paradigm for Integrating Knowledge Graphs with Large Language Models","date":"2024-01-24","arxiv_id":"2401.13444","n_code_links":1,"syntology":null},{"paper":null,"slug":"faster-convergence-with-less-communication","title":"Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks","date":"2024-01-24","arxiv_id":"2401.13779","n_code_links":0,"syntology":null},{"paper":null,"slug":"traffic-learning-and-proactive-uav-trajectory","title":"Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models","date":"2024-01-24","arxiv_id":"2401.13827","n_code_links":0,"syntology":null},{"paper":null,"slug":"digital-cloning-of-online-social-networks-for","title":"Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread","date":"2024-01-23","arxiv_id":"2401.12509","n_code_links":0,"syntology":null},{"paper":null,"slug":"emergent-communication-protocol-learning-for","title":"Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things","date":"2024-01-23","arxiv_id":"2401.12914","n_code_links":0,"syntology":null},{"paper":"/paper/small-language-model-meets-with-reinforced","slug":"small-language-model-meets-with-reinforced","title":"Small Language Model Meets with Reinforced Vision Vocabulary","date":"2024-01-23","arxiv_id":"2401.12503","n_code_links":0,"syntology":null},{"paper":null,"slug":"collaborative-reinforcement-learning-based","title":"Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking","date":"2024-01-22","arxiv_id":"2401.12079","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-learning-and-cycle-consistency","title":"Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation","date":"2024-01-22","arxiv_id":"2401.12340","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalization-and-informativeness-of","title":"Generalization and Informativeness of Conformal Prediction","date":"2024-01-22","arxiv_id":"2401.11810","n_code_links":0,"syntology":null},{"paper":null,"slug":"d2k-turning-historical-data-into-retrievable","title":"D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems","date":"2024-01-21","arxiv_id":"2401.11478","n_code_links":0,"syntology":null},{"paper":null,"slug":"iot-cloud-ran-testbed-for-ultra-precise-tdoa","title":"IoT Cloud RAN Testbed for Ultra-Precise TDoA-based Localization in LPWANs","date":"2024-01-21","arxiv_id":"2401.11435","n_code_links":0,"syntology":null},{"paper":null,"slug":"madrl-based-uavs-trajectory-design-with-anti","title":"MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks","date":"2024-01-21","arxiv_id":"2402.03342","n_code_links":0,"syntology":null},{"paper":"/paper/unim-ov3d-uni-modality-open-vocabulary-3d","slug":"unim-ov3d-uni-modality-open-vocabulary-3d","title":"UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation","date":"2024-01-21","arxiv_id":"2401.11395","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":3,"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":["hithqd/unim-ov3d"],"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":"large-scale-reinforcement-learning-for","title":"Large-scale Reinforcement Learning for Diffusion Models","date":"2024-01-20","arxiv_id":"2401.12244","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-isac-technology-for-uav-sar-imaging","title":"Exploring ISAC Technology for UAV SAR Imaging","date":"2024-01-19","arxiv_id":"2401.10606","n_code_links":0,"syntology":null},{"paper":null,"slug":"sage-hb-swift-adaptation-and-generalization","title":"SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming","date":"2024-01-19","arxiv_id":"2401.10513","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-distributed-stochastic","title":"Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks","date":"2024-01-18","arxiv_id":"2401.09665","n_code_links":0,"syntology":null},{"paper":null,"slug":"autoft-robust-fine-tuning-by-optimizing","title":"AutoFT: Learning an Objective for Robust Fine-Tuning","date":"2024-01-18","arxiv_id":"2401.10220","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-few-shot-segmentation-via-instance","title":"Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention","date":"2024-01-18","arxiv_id":"2401.09866","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-few-shot-semantic-segmentation-via","title":"Boosting Few-Shot Semantic Segmentation Via Segment Anything Model","date":"2024-01-18","arxiv_id":"2401.09826","n_code_links":0,"syntology":null},{"paper":"/paper/cooperative-edge-caching-based-on-elastic","slug":"cooperative-edge-caching-based-on-elastic","title":"Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network","date":"2024-01-18","arxiv_id":"2401.09886","n_code_links":1,"syntology":null},{"paper":null,"slug":"cooperative-tri-point-model-based-ground-to","title":"Cooperative Tri-Point Model-Based Ground-to-Air Coverage Extension in Beyond 5G Networks","date":"2024-01-18","arxiv_id":"2401.09757","n_code_links":0,"syntology":null},{"paper":null,"slug":"curriculum-recommendations-using-transformer","title":"Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method","date":"2024-01-18","arxiv_id":"2401.09699","n_code_links":0,"syntology":null},{"paper":null,"slug":"interplay-of-semantic-communication-and","title":"Interplay of Semantic Communication and Knowledge Learning","date":"2024-01-18","arxiv_id":"2402.03339","n_code_links":0,"syntology":null},{"paper":null,"slug":"matscire-leveraging-pointer-networks-to","title":"MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction","date":"2024-01-18","arxiv_id":"2401.09839","n_code_links":0,"syntology":null},{"paper":"/paper/querying-easily-flip-flopped-samples-for-deep","slug":"querying-easily-flip-flopped-samples-for-deep","title":"Querying Easily Flip-flopped Samples for Deep Active Learning","date":"2024-01-18","arxiv_id":"2401.09787","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 0 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","official":{"repos":["ipcng00/ldm-s"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"tiny-multi-agent-drl-for-twins-migration-in","title":"Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach","date":"2024-01-18","arxiv_id":"2401.09680","n_code_links":0,"syntology":null},{"paper":null,"slug":"antiphishstack-lstm-based-stacked","title":"AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection","date":"2024-01-17","arxiv_id":"2401.08947","n_code_links":0,"syntology":null},{"paper":"/paper/augmenting-math-word-problems-via-iterative","slug":"augmenting-math-word-problems-via-iterative","title":"Augmenting Math Word Problems via Iterative Question Composing","date":"2024-01-17","arxiv_id":"2401.09003","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":["iiis-ai/iterativequestioncomposing"],"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":"reliance-reliable-ensemble-learning-for","title":"RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation","date":"2024-01-17","arxiv_id":"2401.10940","n_code_links":0,"syntology":null},{"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":null,"slug":"towards-continual-learning-desiderata-via","title":"Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding","date":"2024-01-17","arxiv_id":"2401.09067","n_code_links":0,"syntology":null},{"paper":null,"slug":"reinforcement-learning-for-conversational","title":"Reinforcement Learning for Conversational Question Answering over Knowledge Graph","date":"2024-01-16","arxiv_id":"2401.08460","n_code_links":0,"syntology":null},{"paper":null,"slug":"sum-throughput-maximization-in-multi-bd","title":"Sum Throughput Maximization in Multi-BD Symbiotic Radio NOMA Network Assisted by Active-STAR-RIS","date":"2024-01-16","arxiv_id":"2401.08301","n_code_links":0,"syntology":null},{"paper":null,"slug":"top-in-chinese-data-processing-english-code","title":"Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task","date":"2024-01-16","arxiv_id":"2401.10286","n_code_links":0,"syntology":null},{"paper":"/paper/tuning-language-models-by-proxy","slug":"tuning-language-models-by-proxy","title":"Tuning Language Models by Proxy","date":"2024-01-16","arxiv_id":"2401.08565","n_code_links":2,"syntology":{"ran":9,"of":10,"n_ran_checked":7,"n_instrument":2,"unverified":1,"pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["alisawuffles/proxy-tuning"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-strategy-for-implementing-description","title":"A Strategy for Implementing description Temporal Dynamic Algorithms in Dynamic Knowledge Graphs by SPIN","date":"2024-01-15","arxiv_id":"2401.07890","n_code_links":0,"syntology":null},{"paper":null,"slug":"figclip-fine-grained-clip-adaptation-via","title":"FiGCLIP: Fine-Grained CLIP Adaptation via Densely Annotated Videos","date":"2024-01-15","arxiv_id":"2401.07669","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-ocr-quality-in-19th-century","title":"Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach","date":"2024-01-15","arxiv_id":"2401.07787","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-xapp-for-dynamic","title":"Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks","date":"2024-01-15","arxiv_id":"2401.07643","n_code_links":0,"syntology":null},{"paper":"/paper/sciglm-training-scientific-language-models","slug":"sciglm-training-scientific-language-models","title":"SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models","date":"2024-01-15","arxiv_id":"2401.07950","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"pointer_only":8,"phrase":"7 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thudm/sciglm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/candle-iterative-conceptualization-and","slug":"candle-iterative-conceptualization-and","title":"CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning","date":"2024-01-14","arxiv_id":"2401.07286","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hiyouga/llama-factory","hkust-knowcomp/candle"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dual-view-data-hallucination-with-semantic","title":"Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition","date":"2024-01-13","arxiv_id":"2401.07061","n_code_links":0,"syntology":null},{"paper":"/paper/extending-llms-context-window-with-100","slug":"extending-llms-context-window-with-100","title":"Extending LLMs' Context Window with 100 Samples","date":"2024-01-13","arxiv_id":"2401.07004","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gair-nlp/entropy-abf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"pub-a-pragmatics-understanding-benchmark-for","title":"PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities","date":"2024-01-13","arxiv_id":"2401.07078","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-the-applications-of-frontier-ai","title":"A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems","date":"2024-01-12","arxiv_id":"2401.06831","n_code_links":0,"syntology":null},{"paper":"/paper/ada-retrieval-an-adaptive-multi-round","slug":"ada-retrieval-an-adaptive-multi-round","title":"Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations","date":"2024-01-12","arxiv_id":"2401.06633","n_code_links":1,"syntology":{"ran":8,"of":14,"n_ran_checked":7,"n_instrument":1,"unverified":6,"pointer_only":14,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["ll0ruc/ada-retrieval"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":4,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhancements-for-5g-nr-prach-reception-an-ai","title":"Enhancements for 5G NR PRACH Reception: An AI/ML Approach","date":"2024-01-12","arxiv_id":"2401.12803","n_code_links":0,"syntology":null},{"paper":"/paper/inters-unlocking-the-power-of-large-language","slug":"inters-unlocking-the-power-of-large-language","title":"INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning","date":"2024-01-12","arxiv_id":"2401.06532","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":3,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["daod/inters"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ris-aided-nlos-monostatic-sensing-under","title":"RIS-Aided NLoS Monostatic Sensing under Mobility and Angle-Doppler Coupling","date":"2024-01-12","arxiv_id":"2401.06544","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-semioe-ontology-a-semantic-model-solution","title":"The SemIoE Ontology: A Semantic Model Solution for an IoE-based Industry","date":"2024-01-12","arxiv_id":"2401.06667","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-near-field-and-far-field-localization","title":"Unified Near-field and Far-field Localization with Holographic MIMO","date":"2024-01-12","arxiv_id":"2401.06334","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-opportunistic-source-synthesis-method-for","title":"An Opportunistic Source Synthesis Method for Smart Electromagnetic Environments","date":"2024-01-11","arxiv_id":"2401.05993","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-target-to-user","title":"Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems","date":"2024-01-11","arxiv_id":"2401.12801","n_code_links":0,"syntology":null},{"paper":null,"slug":"e-2-gan-efficient-training-of-efficient-gans","title":"E$^{2}$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation","date":"2024-01-11","arxiv_id":"2401.06127","n_code_links":0,"syntology":null},{"paper":null,"slug":"probing-structured-semantics-understanding","title":"How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering","date":"2024-01-11","arxiv_id":"2401.05777","n_code_links":0,"syntology":null},{"paper":"/paper/tone-a-3-tiered-ontology-for-emotion-analysis","slug":"tone-a-3-tiered-ontology-for-emotion-analysis","title":"TONE: A 3-Tiered ONtology for Emotion analysis","date":"2024-01-11","arxiv_id":"2401.06810","n_code_links":1,"syntology":null},{"paper":null,"slug":"ango-a-next-level-evaluation-benchmark-for","title":"ANGO: A Next-Level Evaluation Benchmark For Generation-Oriented Language Models In Chinese Domain","date":"2024-01-10","arxiv_id":"2401.04898","n_code_links":0,"syntology":null},{"paper":null,"slug":"belhd-improving-biomedical-entity-linking","title":"BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation","date":"2024-01-10","arxiv_id":"2401.05125","n_code_links":0,"syntology":null},{"paper":"/paper/closed-form-interpretation-of-neural-network","slug":"closed-form-interpretation-of-neural-network","title":"Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients","date":"2024-01-10","arxiv_id":"2401.04978","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-the-reasoning-abilities-of","title":"Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning","date":"2024-01-10","arxiv_id":"2401.06805","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimising-graph-representation-for-hardware","title":"Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision","date":"2024-01-10","arxiv_id":"2401.04988","n_code_links":0,"syntology":null},{"paper":null,"slug":"adapting-standard-retrieval-benchmarks-to","title":"Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers","date":"2024-01-09","arxiv_id":"2401.04842","n_code_links":0,"syntology":null},{"paper":null,"slug":"class-incremental-learning-for-multi-label","title":"Class-Incremental Learning for Multi-Label Audio Classification","date":"2024-01-09","arxiv_id":"2401.04447","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-neural-network-ensemble","title":"Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment","date":"2024-01-09","arxiv_id":"2401.04748","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-acute-kidney-injury-prediction","title":"Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units","date":"2024-01-09","arxiv_id":"2401.04368","n_code_links":0,"syntology":null}],"record_sha256":"07dd4d7092a6151a60cc39f4b13a52dceb35e73f2a1dfc3c01b1477d75b7dba1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}