{"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/85","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":85,"pages_in_order":106,"rows_per_page":100,"rows":[8401,8500],"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/84","next":"/task/representation-learning/papers/86","papers":[{"url":null,"slug":"quint-node-embedding-using-network-hashing","title":"QUINT: Node embedding using network hashing","date":"2021-09-09","arxiv_id":"2109.04206","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-site-severity-assessment-of-covid-19","title":"Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation","date":"2021-09-08","arxiv_id":"2109.03478","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-mvp-multi-view-prototypical-contrastive","title":"X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning","date":"2021-09-08","arxiv_id":"2109.03560","repositories_listed":0,"syntology":null},{"url":"/paper/rgb-d-salient-object-detection-with","slug":"rgb-d-salient-object-detection-with","title":"RGB-D Salient Object Detection with Ubiquitous Target Awareness","date":"2021-09-08","arxiv_id":"2109.03425","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-contrastive-cross-modality","title":"Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering","date":"2021-09-08","arxiv_id":"2109.03381","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-tumor-segmentation-through","title":"Self-supervised Tumor Segmentation through Layer Decomposition","date":"2021-09-07","arxiv_id":"2109.03230","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dku-dukeece-system-for-the-self","title":"The DKU-DukeECE System for the Self-Supervision Speaker Verification Task of the 2021 VoxCeleb Speaker Recognition Challenge","date":"2021-09-07","arxiv_id":"2109.02853","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theory-guided-heuristic","title":"Information Theory-Guided Heuristic Progressive Multi-View Coding","date":"2021-09-06","arxiv_id":"2109.02344","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointspectrum-equivariance-meets-laplacian","title":"Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning","date":"2021-09-06","arxiv_id":"2109.02358","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-representation-learning-for-video","title":"Multi-modal Representation Learning for Video Advertisement Content Structuring","date":"2021-09-04","arxiv_id":"2109.06637","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-for-efficient-and","title":"Representation Learning for Efficient and Effective Similarity Search and Recommendation","date":"2021-09-04","arxiv_id":"2109.01815","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representation-learning-for-6","title":"Self-supervised Representation Learning for Trip Recommendation","date":"2021-09-02","arxiv_id":"2109.00968","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-manifold-learning-perspective-on","title":"A manifold learning perspective on representation learning: Learning decoder and representations without an encoder","date":"2021-08-31","arxiv_id":"2108.13910","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-multiview-coding-with-electro","title":"Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation","date":"2021-08-31","arxiv_id":"2109.00120","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-neural-network-with-multi","title":"Heterogeneous Graph Neural Network with Multi-view Representation Learning","date":"2021-08-31","arxiv_id":"2108.13650","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-based-hash-embeddings-for-scaling","title":"Position-based Hash Embeddings For Scaling Graph Neural Networks","date":"2021-08-31","arxiv_id":"2109.00101","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-out-of-distribution-generalization-a","title":"Towards Out-Of-Distribution Generalization: A Survey","date":"2021-08-31","arxiv_id":"2108.13624","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-point-solver-for-deep-nonlinear","title":"A theory of representation learning gives a deep generalisation of kernel methods","date":"2021-08-30","arxiv_id":"2108.13097","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibrating-class-activation-maps-for-long","title":"Calibrating Class Activation Maps for Long-Tailed Visual Recognition","date":"2021-08-29","arxiv_id":"2108.12757","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-dive-into-semi-supervised-elbo-for","title":"Deep Dive into Semi-Supervised ELBO for Improving Classification Performance","date":"2021-08-29","arxiv_id":"2108.12734","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-chemical-entity-typing-with","title":"Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation","date":"2021-08-29","arxiv_id":"2108.12899","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-voxelwise-rs-fmri-representation","title":"Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures","date":"2021-08-29","arxiv_id":"2108.12756","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-energy-based-approximate-inference","title":"Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP","date":"2021-08-27","arxiv_id":"2108.12522","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-with-reward","title":"Representation learning with reward prediction errors","date":"2021-08-27","arxiv_id":"2108.12402","repositories_listed":0,"syntology":null},{"url":null,"slug":"drop-dtw-aligning-common-signal-between","title":"Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers","date":"2021-08-26","arxiv_id":"2108.11996","repositories_listed":0,"syntology":null},{"url":null,"slug":"poissonseg-semi-supervised-few-shot-medical","title":"PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning","date":"2021-08-26","arxiv_id":"2108.11694","repositories_listed":0,"syntology":null},{"url":null,"slug":"eta-prediction-with-graph-neural-networks-in","title":"ETA Prediction with Graph Neural Networks in Google Maps","date":"2021-08-25","arxiv_id":"2108.11482","repositories_listed":0,"syntology":null},{"url":"/paper/graph-contrastive-pre-training-for-effective","slug":"graph-contrastive-pre-training-for-effective","title":"Graph Contrastive Pre-training for Effective Theorem Reasoning","date":"2021-08-24","arxiv_id":"2108.10821","repositories_listed":0,"syntology":null},{"url":"/paper/taco-token-aware-cascade-contrastive-learning","slug":"taco-token-aware-cascade-contrastive-learning","title":"TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment","date":"2021-08-23","arxiv_id":"2108.09980","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-network-embedding-via-tensor","title":"Temporal Network Embedding via Tensor Factorization","date":"2021-08-22","arxiv_id":"2108.09837","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-neighborhood-deep-fusion-network-for","title":"Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis","date":"2021-08-20","arxiv_id":"2108.09228","repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-curation-for-unsupervised-contrastive","title":"Batch Curation for Unsupervised Contrastive Representation Learning","date":"2021-08-19","arxiv_id":"2108.08643","repositories_listed":0,"syntology":null},{"url":null,"slug":"category-level-6d-object-pose-estimation-via","title":"Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks","date":"2021-08-19","arxiv_id":"2108.08755","repositories_listed":0,"syntology":null},{"url":"/paper/self-supervised-video-representation-learning-8","slug":"self-supervised-video-representation-learning-8","title":"Self-Supervised Video Representation Learning with Meta-Contrastive Network","date":"2021-08-19","arxiv_id":"2108.08426","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-bidirectional-unsupervised-domain","title":"A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework","date":"2021-08-18","arxiv_id":"2108.07979","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-from-multiple-modalities","title":"Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies","date":"2021-08-18","arxiv_id":"2108.10152","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-variational-learning-for-anomaly","title":"Federated Variational Learning for Anomaly Detection in Multivariate Time Series","date":"2021-08-18","arxiv_id":"2108.08404","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-detect-a-data-driven-approach-for","title":"Learning to Detect: A Data-driven Approach for Network Intrusion Detection","date":"2021-08-18","arxiv_id":"2108.08394","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-visual-representations","title":"Self-Supervised Visual Representations Learning by Contrastive Mask Prediction","date":"2021-08-18","arxiv_id":"2108.07954","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvcnet-multiview-contrastive-network-for","title":"MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions","date":"2021-08-17","arxiv_id":"2108.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-product-search-meets-collaborative","title":"When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach","date":"2021-08-17","arxiv_id":"2108.07574","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-augmented-self-supervised-learning","title":"Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping","date":"2021-08-16","arxiv_id":"2108.07323","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-feature-representations-for-cricket","title":"Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model","date":"2021-08-16","arxiv_id":"2108.07139","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-deep-learning-methods-for-phenotype","title":"Hybrid deep learning methods for phenotype prediction from clinical notes","date":"2021-08-16","arxiv_id":"2108.10682","repositories_listed":0,"syntology":null},{"url":null,"slug":"maps-search-misspelling-detection-leveraging","title":"Maps Search Misspelling Detection Leveraging Domain-Augmented Contextual Representations","date":"2021-08-15","arxiv_id":"2108.06842","repositories_listed":0,"syntology":null},{"url":null,"slug":"voxel-wise-cross-volume-representation","title":"Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction","date":"2021-08-14","arxiv_id":"2108.06522","repositories_listed":0,"syntology":null},{"url":null,"slug":"simcvd-simple-contrastive-voxel-wise","title":"SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation","date":"2021-08-13","arxiv_id":"2108.06227","repositories_listed":0,"syntology":null},{"url":"/paper/billion-scale-pretraining-with-vision","slug":"billion-scale-pretraining-with-vision","title":"Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations","date":"2021-08-12","arxiv_id":"2108.05887","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-bias-invariant-representation-by","title":"Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization","date":"2021-08-11","arxiv_id":"2108.05449","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-strange-attractors-with-reservoir","title":"Learning strange attractors with reservoir systems","date":"2021-08-11","arxiv_id":"2108.05024","repositories_listed":0,"syntology":null},{"url":null,"slug":"clsebert-contrastive-learning-for-syntax","title":"SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation","date":"2021-08-10","arxiv_id":"2108.04556","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-graph-collaborative-filtering","title":"Localized Graph Collaborative Filtering","date":"2021-08-10","arxiv_id":"2108.04475","repositories_listed":0,"syntology":null},{"url":null,"slug":"drinet-a-dual-representation-iterative","title":"DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation","date":"2021-08-09","arxiv_id":"2108.04023","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-representation-learning-for-rapid","title":"Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence","date":"2021-08-08","arxiv_id":"2108.03555","repositories_listed":0,"syntology":null},{"url":null,"slug":"ovis-open-vocabulary-visual-instance-search","title":"OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning","date":"2021-08-08","arxiv_id":"2108.03704","repositories_listed":0,"syntology":null},{"url":null,"slug":"dysr-a-dynamic-representation-learning-and","title":"DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation","date":"2021-08-07","arxiv_id":"2108.03360","repositories_listed":0,"syntology":null},{"url":null,"slug":"missing-data-estimation-in-temporal","title":"Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)","date":"2021-08-07","arxiv_id":"2108.03400","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-discriminative-representation","title":"Towards Discriminative Representation Learning for Unsupervised Person Re-identification","date":"2021-08-07","arxiv_id":"2108.03439","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-normalized-representation-learning","title":"Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing","date":"2021-08-05","arxiv_id":"2108.02667","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-the-information-bottleneck-principle","title":"Applying the Information Bottleneck Principle to Prosodic Representation Learning","date":"2021-08-05","arxiv_id":"2108.02821","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-consensus-based-representation-deep","title":"Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems","date":"2021-08-05","arxiv_id":"2108.02551","repositories_listed":0,"syntology":null},{"url":null,"slug":"security-and-privacy-enhanced-gait","title":"Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers","date":"2021-08-05","arxiv_id":"2108.02400","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-encoder-based-model-for-high-dimensional","title":"Auto-encoder based Model for High-dimensional Imbalanced Industrial Data","date":"2021-08-04","arxiv_id":"2108.02083","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-discriminative-learning-for","title":"Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis","date":"2021-08-04","arxiv_id":"2108.02104","repositories_listed":0,"syntology":null},{"url":null,"slug":"skeleton-cloud-colorization-for-unsupervised","title":"Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning","date":"2021-08-04","arxiv_id":"2108.01959","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-convolutional-networks","title":"Graph Neural Networks With Lifting-based Adaptive Graph Wavelets","date":"2021-08-03","arxiv_id":"2108.01660","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-disentangled-representation-1","title":"Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning","date":"2021-08-02","arxiv_id":"2108.01069","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-representation-learning-strategy","title":"A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"align-voting-behavior-with-public-statements","title":"Align Voting Behavior with Public Statements for Legislator Representation Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogsum-challenge-summarizing-real-life","title":"DialogSum Challenge: Summarizing Real-Life Scenario Dialogues","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-code-representation-learning-for","title":"Disentangled Code Representation Learning for Multiple Programming Languages","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-document-structures-and-cluster","title":"Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-global-information-in-local","title":"Incorporating Global Information in Local Attention for Knowledge Representation Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jcapsr-yi-chong-lian-he-xiao-nang-shen-jing","title":"JCapsR: 一种联合胶囊神经网络的藏语知识图谱表示学习模型(JCapsR: A Joint Capsule Neural Network for Tibetan Knowledge Graph Representation Learning)","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-duo-zhi-xin-yi-zhi-tu-xue-xi-de-she","title":"基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-yi-yuan-biao-shi-xue-xi-de-ci-xiang","title":"基于义原表示学习的词向量表示方法(Word Representation based on Sememe Representation Learning)","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-through-deep","title":"Named Entity Recognition through Deep Representation Learning and Weak Supervision","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-over-entity-action-location-graph","title":"Reasoning over Entity-Action-Location Graph for Procedural Text Understanding","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-style-transfer-leveraging-a-style","title":"Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-representation-learning-using","title":"Fair Representation Learning using Interpolation Enabled Disentanglement","date":"2021-07-31","arxiv_id":"2108.00295","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-vector-functional-link-neural-network-1","title":"Random vector functional link neural network based ensemble deep learning for short-term load forecasting","date":"2021-07-30","arxiv_id":"2107.14385","repositories_listed":0,"syntology":null},{"url":null,"slug":"zooming-into-the-darknet-characterizing","title":"Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes","date":"2021-07-29","arxiv_id":"2108.00079","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-semi-supervised-eeg","title":"Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition","date":"2021-07-28","arxiv_id":"2107.13505","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-stacked-auto-encoders-for-fair","title":"Adversarial Stacked Auto-Encoders for Fair Representation Learning","date":"2021-07-27","arxiv_id":"2107.12826","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-probabilistic-logic-and-deep","title":"Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning","date":"2021-07-27","arxiv_id":"2107.12591","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-generative-representation","title":"Discriminative-Generative Representation Learning for One-Class Anomaly Detection","date":"2021-07-27","arxiv_id":"2107.12753","repositories_listed":0,"syntology":null},{"url":null,"slug":"alleviate-representation-overlapping-in-class","title":"Revisiting Catastrophic Forgetting in Class Incremental Learning","date":"2021-07-26","arxiv_id":"2107.12308","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-wav2vec2-an-application-of","title":"An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning","date":"2021-07-26","arxiv_id":"2107.13530","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-bilevel-optimization-via-bregman","title":"Enhanced Bilevel Optimization via Bregman Distance","date":"2021-07-26","arxiv_id":"2107.12301","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-on-tissue","title":"Graph Representation Learning on Tissue-Specific Multi-Omics","date":"2021-07-25","arxiv_id":"2107.11856","repositories_listed":0,"syntology":null},{"url":null,"slug":"wip-abstract-robust-out-of-distribution","title":"WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS","date":"2021-07-25","arxiv_id":"2107.11736","repositories_listed":0,"syntology":null},{"url":"/paper/clustering-by-maximizing-mutual-information","slug":"clustering-by-maximizing-mutual-information","title":"Clustering by Maximizing Mutual Information Across Views","date":"2021-07-24","arxiv_id":"2107.11635","repositories_listed":0,"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":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/clustering-by-maximizing-mutual-information#ran","syntology_url":"https://syntology.ai/paper/2107.11635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11635"}},"official":null}},{"url":null,"slug":"localglmnet-interpretable-deep-learning-for","title":"LocalGLMnet: interpretable deep learning for tabular data","date":"2021-07-23","arxiv_id":"2107.11059","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-classification-and-clustering-with","title":"Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss","date":"2021-07-23","arxiv_id":"2107.14597","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-considerations-in-graph-representation","title":"Data Considerations in Graph Representation Learning for Supply Chain Networks","date":"2021-07-22","arxiv_id":"2107.10609","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-artificial-intelligence-natural-language","title":"Neuradicon: operational representation learning of neuroimaging reports","date":"2021-07-21","arxiv_id":"2107.10021","repositories_listed":0,"syntology":null},{"url":null,"slug":"mg-net-leveraging-pseudo-imaging-for-multi","title":"MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis","date":"2021-07-21","arxiv_id":"2107.09883","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-representations-learning-and","title":"Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction","date":"2021-07-21","arxiv_id":"2107.09928","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-small-but-meaningful-representations","title":"Creating small but meaningful representations of digital pathology images","date":"2021-07-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cetransformer-casual-effect-estimation-via","title":"CETransformer: Casual Effect Estimation via Transformer Based Representation Learning","date":"2021-07-19","arxiv_id":"2107.08714","repositories_listed":0,"syntology":null}],"record_sha256":"579488e54910f3c3b7d20315a37a08743eb1cb76a14e1b49751ac5555848c93c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}