{"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/representation-learning/papers/9","list_of":"/task/representation-learning","task":"Representation Learning","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":9,"pages_in_order":106,"rows_per_page":100,"rows":[801,900],"of":10580,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"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/representation-learning","prev":"/task/representation-learning/papers/8","next":"/task/representation-learning/papers/10","papers":[{"url":"/paper/riemannian-generative-decoder","slug":"riemannian-generative-decoder","title":"Riemannian generative decoder","date":"2025-06-23","arxiv_id":"2506.19133","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/riemannian-generative-decoder#ran","syntology_url":"https://syntology.ai/paper/2506.19133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.19133"}},"official":{"repos":["yhsure/riemannian-generative-decoder"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/these-are-not-all-the-features-you-are","slug":"these-are-not-all-the-features-you-are","title":"These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised Pretraining","date":"2025-06-23","arxiv_id":"2506.18221","repositories_listed":1,"syntology":null},{"url":"/paper/eccdnamamba-a-pre-trained-model-for-ultra","slug":"eccdnamamba-a-pre-trained-model-for-ultra","title":"eccDNAMamba: A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis","date":"2025-06-22","arxiv_id":"2506.18940","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/eccdnamamba-a-pre-trained-model-for-ultra#ran","syntology_url":"https://syntology.ai/paper/2506.18940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.18940"}},"official":{"repos":["zzq1zh/genai-lab"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/cross-modal-epileptic-signal-harmonization","slug":"cross-modal-epileptic-signal-harmonization","title":"Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer","date":"2025-06-20","arxiv_id":"2506.17068","repositories_listed":1,"syntology":null},{"url":"/paper/unifork-exploring-modality-alignment-for","slug":"unifork-exploring-modality-alignment-for","title":"UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation","date":"2025-06-20","arxiv_id":"2506.17202","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"8 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unifork-exploring-modality-alignment-for#ran","syntology_url":"https://syntology.ai/paper/2506.17202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.17202"}},"official":{"repos":["tliby/unifork"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cloud-a-scalable-and-physics-informed","slug":"cloud-a-scalable-and-physics-informed","title":"CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning","date":"2025-06-19","arxiv_id":"2506.17345","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-score-based-priors-for","slug":"expressive-score-based-priors-for","title":"Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization","date":"2025-06-17","arxiv_id":"2506.14607","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/expressive-score-based-priors-for#ran","syntology_url":"https://syntology.ai/paper/2506.14607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14607"}},"official":{"repos":["inouye-lab/saub"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/into-the-unknown-applying-inductive-spatial","slug":"into-the-unknown-applying-inductive-spatial","title":"Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places","date":"2025-06-17","arxiv_id":"2506.14070","repositories_listed":1,"syntology":null},{"url":"/paper/come-adding-scene-centric-forecasting-control","slug":"come-adding-scene-centric-forecasting-control","title":"COME: Adding Scene-Centric Forecasting Control to Occupancy World Model","date":"2025-06-16","arxiv_id":"2506.13260","repositories_listed":1,"syntology":null},{"url":"/paper/beyondrpc-a-contrastive-and-augmentation","slug":"beyondrpc-a-contrastive-and-augmentation","title":"BeyondRPC: A Contrastive and Augmentation-Driven Framework for Robust Point Cloud Understanding","date":"2025-06-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/latent-representation-learning-of-multi-scale","slug":"latent-representation-learning-of-multi-scale","title":"Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material","date":"2025-06-15","arxiv_id":"2506.12996","repositories_listed":1,"syntology":null},{"url":"/paper/improving-large-language-model-safety-with","slug":"improving-large-language-model-safety-with","title":"Improving Large Language Model Safety with Contrastive Representation Learning","date":"2025-06-13","arxiv_id":"2506.11938","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-echocardiographic","slug":"self-supervised-learning-of-echocardiographic","title":"Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation","date":"2025-06-13","arxiv_id":"2506.11777","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":3,"n_ran_checked":3,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-learning-of-echocardiographic#ran","syntology_url":"https://syntology.ai/paper/2506.11777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.11777"}},"official":{"repos":["mdivyanshu97/discovr"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-hierarchical-latent-capabilities","slug":"discovering-hierarchical-latent-capabilities","title":"Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning","date":"2025-06-12","arxiv_id":"2506.10378","repositories_listed":1,"syntology":null},{"url":"/paper/structural-spectral-graph-convolution-with","slug":"structural-spectral-graph-convolution-with","title":"Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering","date":"2025-06-11","arxiv_id":"2506.09920","repositories_listed":1,"syntology":null},{"url":"/paper/unipre3d-unified-pre-training-of-3d-point-1","slug":"unipre3d-unified-pre-training-of-3d-point-1","title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting","date":"2025-06-11","arxiv_id":"2506.09952","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/unipre3d-unified-pre-training-of-3d-point-1#ran","syntology_url":"https://syntology.ai/paper/2506.09952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09952"}},"official":{"repos":["wangzy22/unipre3d"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/2506-08884","slug":"2506-08884","title":"InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis","date":"2025-06-10","arxiv_id":"2506.08884","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-counterfactual-generation-with","slug":"diffusion-counterfactual-generation-with","title":"Diffusion Counterfactual Generation with Semantic Abduction","date":"2025-06-09","arxiv_id":"2506.07883","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffusion-counterfactual-generation-with#ran","syntology_url":"https://syntology.ai/paper/2506.07883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.07883"}},"official":{"repos":["rajatrasal/diffusion-counterfactuals"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-sequence-models-for-enhanced","slug":"diffusion-sequence-models-for-enhanced","title":"Diffusion Sequence Models for Enhanced Protein Representation and Generation","date":"2025-06-09","arxiv_id":"2506.08293","repositories_listed":1,"syntology":null},{"url":"/paper/multiple-object-stitching-for-unsupervised","slug":"multiple-object-stitching-for-unsupervised","title":"Multiple Object Stitching for Unsupervised Representation Learning","date":"2025-06-09","arxiv_id":"2506.07364","repositories_listed":1,"syntology":null},{"url":"/paper/annodpo-protein-functional-annotation","slug":"annodpo-protein-functional-annotation","title":"AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization","date":"2025-06-08","arxiv_id":"2506.07035","repositories_listed":1,"syntology":null},{"url":"/paper/positional-encoding-meets-persistent-homology","slug":"positional-encoding-meets-persistent-homology","title":"Positional Encoding meets Persistent Homology on Graphs","date":"2025-06-06","arxiv_id":"2506.05814","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/positional-encoding-meets-persistent-homology#ran","syntology_url":"https://syntology.ai/paper/2506.05814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05814"}},"official":{"repos":["aalto-quml/pipe"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/polaris-a-high-contrast-polarimetric-imaging","slug":"polaris-a-high-contrast-polarimetric-imaging","title":"POLARIS: A High-contrast Polarimetric Imaging Benchmark Dataset for Exoplanetary Disk Representation Learning","date":"2025-06-04","arxiv_id":"2506.03511","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/polaris-a-high-contrast-polarimetric-imaging#ran","syntology_url":"https://syntology.ai/paper/2506.03511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.03511"}},"official":{"repos":["astraeus999/POLARIS_img_analysis"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/textatari-100k-frames-game-playing-with","slug":"textatari-100k-frames-game-playing-with","title":"TextAtari: 100K Frames Game Playing with Language Agents","date":"2025-06-04","arxiv_id":"2506.04098","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/textatari-100k-frames-game-playing-with#ran","syntology_url":"https://syntology.ai/paper/2506.04098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.04098"}},"official":{"repos":["Lww007/Text-Atari-Agents"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/simple-good-fast-self-supervised-world-models","slug":"simple-good-fast-self-supervised-world-models","title":"Simple, Good, Fast: Self-Supervised World Models Free of Baggage","date":"2025-06-03","arxiv_id":"2506.02612","repositories_listed":1,"syntology":{"n":34,"n_ran":26,"n_constructed":4,"n_ran_checked":8,"n_instrument":18,"n_unverified":8,"n_honours":2,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"26 ran (of which 4 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 1 violated, 5 with no contract checked; 18 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/simple-good-fast-self-supervised-world-models#ran","syntology_url":"https://syntology.ai/paper/2506.02612","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.02612"}},"official":{"repos":["jrobine/sgf"],"state":"official (archive's flag): 26 ran","n_ran":26,"n_constructed":4,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/context-is-gold-to-find-the-gold-passage","slug":"context-is-gold-to-find-the-gold-passage","title":"Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings","date":"2025-05-30","arxiv_id":"2505.24782","repositories_listed":1,"syntology":null},{"url":"/paper/on-designing-diffusion-autoencoders-for","slug":"on-designing-diffusion-autoencoders-for","title":"On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning","date":"2025-05-30","arxiv_id":"2506.00136","repositories_listed":1,"syntology":null},{"url":"/paper/deepchest-dynamic-gradient-free-task","slug":"deepchest-dynamic-gradient-free-task","title":"DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification","date":"2025-05-29","arxiv_id":"2505.23595","repositories_listed":1,"syntology":null},{"url":"/paper/frera-a-frequency-refined-augmentation-for","slug":"frera-a-frequency-refined-augmentation-for","title":"FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification","date":"2025-05-29","arxiv_id":"2505.23181","repositories_listed":1,"syntology":null},{"url":"/paper/lemore-learn-more-details-for-lightweight","slug":"lemore-learn-more-details-for-lightweight","title":"LeMoRe: Learn More Details for Lightweight Semantic Segmentation","date":"2025-05-29","arxiv_id":"2505.23093","repositories_listed":1,"syntology":null},{"url":"/paper/qlip-a-dynamic-quadtree-vision-prior-enhances","slug":"qlip-a-dynamic-quadtree-vision-prior-enhances","title":"QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining","date":"2025-05-29","arxiv_id":"2505.23004","repositories_listed":1,"syntology":null},{"url":"/paper/subgraph-gaussian-embedding-contrast-for-self","slug":"subgraph-gaussian-embedding-contrast-for-self","title":"Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning","date":"2025-05-29","arxiv_id":"2505.23529","repositories_listed":1,"syntology":null},{"url":"/paper/baryir-learning-multi-source-unified","slug":"baryir-learning-multi-source-unified","title":"BaryIR: Learning Multi-Source Unified Representation in Continuous Barycenter Space for Generalizable All-in-One Image Restoration","date":"2025-05-27","arxiv_id":"2505.21637","repositories_listed":1,"syntology":null},{"url":"/paper/agentic-predictor-performance-prediction-for","slug":"agentic-predictor-performance-prediction-for","title":"Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding","date":"2025-05-26","arxiv_id":"2505.19764","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/agentic-predictor-performance-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2505.19764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19764"}},"official":{"repos":["deepauto-ai/agentic-predictor"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/discrete-markov-bridge","slug":"discrete-markov-bridge","title":"Discrete Markov Bridge","date":"2025-05-26","arxiv_id":"2505.19752","repositories_listed":1,"syntology":null},{"url":"/paper/langdaug-langevin-data-augmentation-for-multi","slug":"langdaug-langevin-data-augmentation-for-multi","title":"LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation","date":"2025-05-26","arxiv_id":"2505.19659","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/langdaug-langevin-data-augmentation-for-multi#ran","syntology_url":"https://syntology.ai/paper/2505.19659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19659"}},"official":{"repos":["backpropagator/langdaug"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/modality-curation-building-universal","slug":"modality-curation-building-universal","title":"Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval","date":"2025-05-26","arxiv_id":"2505.19650","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/modality-curation-building-universal#ran","syntology_url":"https://syntology.ai/paper/2505.19650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19650"}},"official":{"repos":["friedrichor/UNITE"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/style2code-a-style-controllable-code","slug":"style2code-a-style-controllable-code","title":"Style2Code: A Style-Controllable Code Generation Framework with Dual-Modal Contrastive Representation Learning","date":"2025-05-26","arxiv_id":"2505.19442","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-video-self-supervised-learning-via","slug":"advancing-video-self-supervised-learning-via","title":"Advancing Video Self-Supervised Learning via Image Foundation Models","date":"2025-05-25","arxiv_id":"2505.19218","repositories_listed":1,"syntology":null},{"url":"/paper/amorlip-efficient-language-image-pretraining","slug":"amorlip-efficient-language-image-pretraining","title":"AmorLIP: Efficient Language-Image Pretraining via Amortization","date":"2025-05-25","arxiv_id":"2505.18983","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-evolution-operator-learning","slug":"self-supervised-evolution-operator-learning","title":"Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems","date":"2025-05-24","arxiv_id":"2505.18671","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-evolution-operator-learning#ran","syntology_url":"https://syntology.ai/paper/2505.18671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18671"}},"official":{"repos":["pietronvll/encoderops"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/neighbour-driven-gaussian-process-variational","slug":"neighbour-driven-gaussian-process-variational","title":"Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling","date":"2025-05-22","arxiv_id":"2505.16481","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/neighbour-driven-gaussian-process-variational#ran","syntology_url":"https://syntology.ai/paper/2505.16481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16481"}},"official":{"repos":["shixinxing/nngpvae-official"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/auxdet-auxiliary-metadata-matters-for-omni","slug":"auxdet-auxiliary-metadata-matters-for-omni","title":"AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection","date":"2025-05-21","arxiv_id":"2505.15184","repositories_listed":1,"syntology":null},{"url":"/paper/uwsam-segment-anything-model-guided","slug":"uwsam-segment-anything-model-guided","title":"UWSAM: Segment Anything Model Guided Underwater Instance Segmentation and A Large-scale Benchmark Dataset","date":"2025-05-21","arxiv_id":"2505.15581","repositories_listed":1,"syntology":null},{"url":"/paper/lobstur-a-local-bootstrap-framework-for","slug":"lobstur-a-local-bootstrap-framework-for","title":"LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks","date":"2025-05-20","arxiv_id":"2505.14867","repositories_listed":1,"syntology":null},{"url":"/paper/picturized-and-recited-with-dialects-a","slug":"picturized-and-recited-with-dialects-a","title":"Picturized and Recited with Dialects: A Multimodal Chinese Representation Framework for Sentiment Analysis of Classical Chinese Poetry","date":"2025-05-19","arxiv_id":"2505.13210","repositories_listed":1,"syntology":null},{"url":"/paper/ditch-the-denoiser-emergence-of-noise","slug":"ditch-the-denoiser-emergence-of-noise","title":"Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum","date":"2025-05-18","arxiv_id":"2505.12191","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/ditch-the-denoiser-emergence-of-noise#ran","syntology_url":"https://syntology.ai/paper/2505.12191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.12191"}},"official":{"repos":["wenquanlu/noisy_dinov2"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/unsupervised-invariant-risk-minimization","slug":"unsupervised-invariant-risk-minimization","title":"Unsupervised Invariant Risk Minimization","date":"2025-05-18","arxiv_id":"2505.12506","repositories_listed":1,"syntology":null},{"url":"/paper/2dnmrgym-an-annotated-experimental-dataset","slug":"2dnmrgym-an-annotated-experimental-dataset","title":"2DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision","date":"2025-05-16","arxiv_id":"2505.18181","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-representations-via-wasserstein","slug":"invariant-representations-via-wasserstein","title":"Invariant Representations via Wasserstein Correlation Maximization","date":"2025-05-16","arxiv_id":"2505.11702","repositories_listed":1,"syntology":null},{"url":"/paper/questioning-representational-optimism-in-deep","slug":"questioning-representational-optimism-in-deep","title":"Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis","date":"2025-05-16","arxiv_id":"2505.11581","repositories_listed":1,"syntology":null},{"url":"/paper/mmrl-parameter-efficient-and-interaction","slug":"mmrl-parameter-efficient-and-interaction","title":"MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models","date":"2025-05-15","arxiv_id":"2505.10088","repositories_listed":1,"syntology":null},{"url":"/paper/radiogenomic-bipartite-graph-representation","slug":"radiogenomic-bipartite-graph-representation","title":"Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection","date":"2025-05-14","arxiv_id":"2505.09848","repositories_listed":1,"syntology":null},{"url":"/paper/stable-and-convexified-information-bottleneck","slug":"stable-and-convexified-information-bottleneck","title":"Stable and Convexified Information Bottleneck Optimization via Symbolic Continuation and Entropy-Regularized Trajectories","date":"2025-05-14","arxiv_id":"2505.09239","repositories_listed":1,"syntology":null},{"url":"/paper/olinear-a-linear-model-for-time-series","slug":"olinear-a-linear-model-for-time-series","title":"OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain","date":"2025-05-12","arxiv_id":"2505.08550","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-with-mutual-influence","slug":"representation-learning-with-mutual-influence","title":"Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks","date":"2025-05-12","arxiv_id":"2505.07895","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-think-bootstrapping-llm-reasoning","slug":"learn-to-think-bootstrapping-llm-reasoning","title":"Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning","date":"2025-05-09","arxiv_id":"2505.06321","repositories_listed":1,"syntology":null},{"url":"/paper/owt-a-foundational-organ-wise-tokenization","slug":"owt-a-foundational-organ-wise-tokenization","title":"OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging","date":"2025-05-08","arxiv_id":"2505.04899","repositories_listed":1,"syntology":null},{"url":"/paper/pytdc-a-multimodal-machine-learning-training","slug":"pytdc-a-multimodal-machine-learning-training","title":"PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models","date":"2025-05-08","arxiv_id":"2505.05577","repositories_listed":1,"syntology":null},{"url":"/paper/cogenav-versatile-audio-visual-representation","slug":"cogenav-versatile-audio-visual-representation","title":"CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization","date":"2025-05-06","arxiv_id":"2505.03186","repositories_listed":1,"syntology":null},{"url":"/paper/fastabx-a-library-for-efficient-computation","slug":"fastabx-a-library-for-efficient-computation","title":"fastabx: A library for efficient computation of ABX discriminability","date":"2025-05-05","arxiv_id":"2505.02692","repositories_listed":1,"syntology":null},{"url":"/paper/geoerm-geometry-aware-multi-task","slug":"geoerm-geometry-aware-multi-task","title":"GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds","date":"2025-05-05","arxiv_id":"2505.02972","repositories_listed":1,"syntology":null},{"url":"/paper/no-other-representation-component-is-needed","slug":"no-other-representation-component-is-needed","title":"No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves","date":"2025-05-05","arxiv_id":"2505.02831","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/no-other-representation-component-is-needed#ran","syntology_url":"https://syntology.ai/paper/2505.02831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.02831"}},"official":{"repos":["vvvvvjdy/sra"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vaemo-efficient-representation-learning-for","slug":"vaemo-efficient-representation-learning-for","title":"VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection","date":"2025-05-05","arxiv_id":"2505.02331","repositories_listed":1,"syntology":null},{"url":"/paper/nbbench-benchmarking-language-models-for","slug":"nbbench-benchmarking-language-models-for","title":"NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks","date":"2025-05-04","arxiv_id":"2505.02022","repositories_listed":1,"syntology":null},{"url":"/paper/spectrumfm-a-foundation-model-for-intelligent","slug":"spectrumfm-a-foundation-model-for-intelligent","title":"SpectrumFM: A Foundation Model for Intelligent Spectrum Management","date":"2025-05-02","arxiv_id":"2505.06256","repositories_listed":1,"syntology":null},{"url":"/paper/cse-sfp-enabling-unsupervised-sentence","slug":"cse-sfp-enabling-unsupervised-sentence","title":"CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass","date":"2025-05-01","arxiv_id":"2505.00389","repositories_listed":1,"syntology":null},{"url":"/paper/abg-nas-adaptive-bayesian-genetic-neural","slug":"abg-nas-adaptive-bayesian-genetic-neural","title":"ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning","date":"2025-04-30","arxiv_id":"2504.21254","repositories_listed":1,"syntology":null},{"url":"/paper/recursive-kl-divergence-optimization-a","slug":"recursive-kl-divergence-optimization-a","title":"Recursive KL Divergence Optimization: A Dynamic Framework for Representation Learning","date":"2025-04-30","arxiv_id":"2504.21707","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-preserving","slug":"representation-learning-preserving","title":"Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects","date":"2025-04-29","arxiv_id":"2504.20579","repositories_listed":1,"syntology":null},{"url":"/paper/learning-hierarchical-interaction-for","slug":"learning-hierarchical-interaction-for","title":"Learning Hierarchical Interaction for Accurate Molecular Property Prediction","date":"2025-04-28","arxiv_id":"2504.20127","repositories_listed":1,"syntology":null},{"url":"/paper/feature-fusion-revisited-multimodal-ctr","slug":"feature-fusion-revisited-multimodal-ctr","title":"Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge","date":"2025-04-26","arxiv_id":"2504.18961","repositories_listed":1,"syntology":null},{"url":"/paper/tsrm-a-lightweight-temporal-feature-encoding","slug":"tsrm-a-lightweight-temporal-feature-encoding","title":"TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation","date":"2025-04-26","arxiv_id":"2504.18878","repositories_listed":1,"syntology":null},{"url":"/paper/quadratic-interest-network-for-multimodal","slug":"quadratic-interest-network-for-multimodal","title":"Quadratic Interest Network for Multimodal Click-Through Rate Prediction","date":"2025-04-24","arxiv_id":"2504.17699","repositories_listed":1,"syntology":null},{"url":"/paper/pointlora-low-rank-adaptation-with-token-1","slug":"pointlora-low-rank-adaptation-with-token-1","title":"PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning","date":"2025-04-22","arxiv_id":"2504.16023","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pointlora-low-rank-adaptation-with-token-1#ran","syntology_url":"https://syntology.ai/paper/2504.16023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.16023"}},"official":{"repos":["songw-zju/pointlora"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/distribution-aware-forgetting-compensation","slug":"distribution-aware-forgetting-compensation","title":"Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification","date":"2025-04-21","arxiv_id":"2504.15041","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-degree-bias-in-graph","slug":"mitigating-degree-bias-in-graph","title":"Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention","date":"2025-04-21","arxiv_id":"2504.15075","repositories_listed":1,"syntology":null},{"url":"/paper/chexworld-exploring-image-world-modeling-for","slug":"chexworld-exploring-image-world-modeling-for","title":"CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning","date":"2025-04-18","arxiv_id":"2504.13820","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/chexworld-exploring-image-world-modeling-for#ran","syntology_url":"https://syntology.ai/paper/2504.13820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.13820"}},"official":{"repos":["LeapLabTHU/CheXWorld"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-learning-for-tabular-data-a","slug":"representation-learning-for-tabular-data-a","title":"Representation Learning for Tabular Data: A Comprehensive Survey","date":"2025-04-17","arxiv_id":"2504.16109","repositories_listed":1,"syntology":null},{"url":"/paper/gt-svq-a-linear-time-graph-transformer-for","slug":"gt-svq-a-linear-time-graph-transformer-for","title":"GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization","date":"2025-04-16","arxiv_id":"2504.11840","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-structural-and-semantic-signals","slug":"integrating-structural-and-semantic-signals","title":"Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex","date":"2025-04-16","arxiv_id":"2504.12474","repositories_listed":1,"syntology":null},{"url":"/paper/elucidating-the-design-space-of-multimodal","slug":"elucidating-the-design-space-of-multimodal","title":"Elucidating the Design Space of Multimodal Protein Language Models","date":"2025-04-15","arxiv_id":"2504.11454","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/elucidating-the-design-space-of-multimodal#ran","syntology_url":"https://syntology.ai/paper/2504.11454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.11454"}},"official":null}},{"url":"/paper/a-model-zoo-of-vision-transformers","slug":"a-model-zoo-of-vision-transformers","title":"A Model Zoo of Vision Transformers","date":"2025-04-14","arxiv_id":"2504.10231","repositories_listed":1,"syntology":null},{"url":"/paper/negate-or-embrace-on-how-misalignment-shapes","slug":"negate-or-embrace-on-how-misalignment-shapes","title":"On the Value of Cross-Modal Misalignment in Multimodal Representation Learning","date":"2025-04-14","arxiv_id":"2504.10143","repositories_listed":1,"syntology":null},{"url":"/paper/causal-integration-of-chemical-structures","slug":"causal-integration-of-chemical-structures","title":"Causal integration of chemical structures improves representations of microscopy images for morphological profiling","date":"2025-04-13","arxiv_id":"2504.09544","repositories_listed":1,"syntology":null},{"url":"/paper/nettag-a-multimodal-rtl-and-layout-aligned","slug":"nettag-a-multimodal-rtl-and-layout-aligned","title":"NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph","date":"2025-04-12","arxiv_id":"2504.09260","repositories_listed":1,"syntology":null},{"url":"/paper/gigatok-scaling-visual-tokenizers-to-3","slug":"gigatok-scaling-visual-tokenizers-to-3","title":"GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation","date":"2025-04-11","arxiv_id":"2504.08736","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":7,"n_ran_checked":7,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":15,"phrase":"10 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/gigatok-scaling-visual-tokenizers-to-3#ran","syntology_url":"https://syntology.ai/paper/2504.08736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.08736"}},"official":{"repos":["SilentView/GigaTok"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/latent-diffusion-autoencoders-toward","slug":"latent-diffusion-autoencoders-toward","title":"Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging","date":"2025-04-11","arxiv_id":"2504.08635","repositories_listed":1,"syntology":null},{"url":"/paper/local-distance-preserving-node-embeddings-and","slug":"local-distance-preserving-node-embeddings-and","title":"Local Distance-Preserving Node Embeddings and Their Performance on Random Graphs","date":"2025-04-11","arxiv_id":"2504.08216","repositories_listed":1,"syntology":null},{"url":"/paper/medrep-medical-concept-representation-for","slug":"medrep-medical-concept-representation-for","title":"MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models","date":"2025-04-11","arxiv_id":"2504.08329","repositories_listed":1,"syntology":null},{"url":"/paper/defending-llm-watermarking-against-spoofing","slug":"defending-llm-watermarking-against-spoofing","title":"Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning","date":"2025-04-09","arxiv_id":"2504.06575","repositories_listed":1,"syntology":null},{"url":"/paper/robo-taxi-fleet-coordination-at-scale-via","slug":"robo-taxi-fleet-coordination-at-scale-via","title":"Robo-taxi Fleet Coordination at Scale via Reinforcement Learning","date":"2025-04-08","arxiv_id":"2504.06125","repositories_listed":1,"syntology":null},{"url":"/paper/cohesion-composite-graph-convolutional","slug":"cohesion-composite-graph-convolutional","title":"COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation","date":"2025-04-06","arxiv_id":"2504.04452","repositories_listed":1,"syntology":null},{"url":"/paper/squeeze-and-excitation-a-weighted-graph","slug":"squeeze-and-excitation-a-weighted-graph","title":"Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering","date":"2025-04-06","arxiv_id":"2504.04443","repositories_listed":1,"syntology":null},{"url":"/paper/dual-stream-transformer-gcn-model-with","slug":"dual-stream-transformer-gcn-model-with","title":"Dual-stream Transformer-GCN Model with Contextualized Representations Learning for Monocular 3D Human Pose Estimation","date":"2025-04-02","arxiv_id":"2504.01764","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-normalize-on-the-spd-manifold-1","slug":"learning-to-normalize-on-the-spd-manifold-1","title":"Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry","date":"2025-04-01","arxiv_id":"2504.00660","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/learning-to-normalize-on-the-spd-manifold-1#ran","syntology_url":"https://syntology.ai/paper/2504.00660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.00660"}},"official":null}},{"url":"/paper/mergevq-a-unified-framework-for-visual","slug":"mergevq-a-unified-framework-for-visual","title":"MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization","date":"2025-04-01","arxiv_id":"2504.00999","repositories_listed":1,"syntology":null},{"url":"/paper/smile-infusing-spatial-and-motion-semantics","slug":"smile-infusing-spatial-and-motion-semantics","title":"SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning","date":"2025-04-01","arxiv_id":"2504.00527","repositories_listed":1,"syntology":null},{"url":"/paper/lorentzian-graph-isomorphic-network","slug":"lorentzian-graph-isomorphic-network","title":"LGIN: Defining an Approximately Powerful Hyperbolic GNN","date":"2025-03-31","arxiv_id":"2504.00142","repositories_listed":1,"syntology":null},{"url":"/paper/msngo-multi-species-protein-function","slug":"msngo-multi-species-protein-function","title":"MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation","date":"2025-03-29","arxiv_id":"2503.23014","repositories_listed":1,"syntology":null}],"record_sha256":"4d0f9ddd76bc6b4cff9f4477916ab8d29fdae9e974dfbd55f67f0b297f24a0b8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}