{"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/multi-task-learning/papers/19","list_of":"/task/multi-task-learning","task":"Multi-Task 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":19,"pages_in_order":37,"rows_per_page":100,"rows":[1801,1900],"of":3687,"counts":{"archive_papers_tagged":3687,"with_a_code_link":1306,"where_syntology_ran_a_sample":260,"not_listed_spam_title":0,"listed":3687,"listed_where_code_ran":260,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":217,"every_run_a_failure_of_syntologys_instrument":43,"listed_with_a_run_with_no_instrument_failure":217,"listed_every_run_a_failure_of_syntologys_instrument":43,"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/multi-task-learning","prev":"/task/multi-task-learning/papers/18","next":"/task/multi-task-learning/papers/20","papers":[{"url":null,"slug":"learning-to-optimize-by-multi-gradient-for","title":"Learning to optimize by multi-gradient for multi-objective optimization","date":"2023-11-01","arxiv_id":"2311.00559","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-limitations-of-state-aware","title":"Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving","date":"2023-10-31","arxiv_id":"2310.20650","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamically-updating-event-representations-1","title":"Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning","date":"2023-10-31","arxiv_id":"2310.20236","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-of-convex-combinations-of","title":"Optimizing accuracy and diversity: a multi-task approach to forecast combinations","date":"2023-10-31","arxiv_id":"2310.20545","repositories_listed":0,"syntology":null},{"url":null,"slug":"bioinstruct-instruction-tuning-of-large","title":"BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing","date":"2023-10-30","arxiv_id":"2310.19975","repositories_listed":0,"syntology":null},{"url":"/paper/dining-on-details-llm-guided-expert-networks","slug":"dining-on-details-llm-guided-expert-networks","title":"Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition","date":"2023-10-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"playing-in-the-dark-no-regret-learning-with","title":"Optimal Algorithms for Online Convex Optimization with Adversarial Constraints","date":"2023-10-29","arxiv_id":"2310.18955","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-segment-to-segment-framework-for","title":"Unified Segment-to-Segment Framework for Simultaneous Sequence Generation","date":"2023-10-27","arxiv_id":"2310.17940","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-knowledge-base-canonicalization-with","title":"Open Knowledge Base Canonicalization with Multi-task Unlearning","date":"2023-10-25","arxiv_id":"2310.16419","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-chronicles-how-linguistic","title":"Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training","date":"2023-10-25","arxiv_id":"2310.16484","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-communication-theory-perspective-on","title":"A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models","date":"2023-10-24","arxiv_id":"2310.18358","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-grouping-for-automated-multi-task","title":"Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction","date":"2023-10-24","arxiv_id":"2310.16241","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam-clip-merging-vision-foundation-models","title":"SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding","date":"2023-10-23","arxiv_id":"2310.15308","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairbranch-fairness-conflict-correction-on","title":"FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning","date":"2023-10-20","arxiv_id":"2310.13746","repositories_listed":0,"syntology":null},{"url":null,"slug":"motif-based-prompt-learning-for-universal","title":"Motif-Based Prompt Learning for Universal Cross-Domain Recommendation","date":"2023-10-20","arxiv_id":"2310.13303","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-benefits-of-multi-task-rl-under-non","title":"Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes","date":"2023-10-20","arxiv_id":"2310.13550","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcrnn-a-deep-cross-approach-based-on-rnn-for","title":"DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning","date":"2023-10-18","arxiv_id":"2310.11777","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-results-of-underwater-sound","title":"Experimental Results of Underwater Sound Speed Profile Inversion by Few-shot Multi-task Learning","date":"2023-10-18","arxiv_id":"2310.11708","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-and-discovering-quantum-properties","title":"Learning quantum properties from short-range correlations using multi-task networks","date":"2023-10-18","arxiv_id":"2310.11807","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-neural-ranking-framework-toward","title":"Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems","date":"2023-10-16","arxiv_id":"2310.10462","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-multi-objective-learning","title":"Federated Multi-Objective Learning","date":"2023-10-15","arxiv_id":"2310.09866","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-advances-in-body-composition-assessment","title":"New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach","date":"2023-10-15","arxiv_id":"2310.09709","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-latent-spaces-facilitate-data","title":"Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning","date":"2023-10-13","arxiv_id":"2310.09278","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-bias-for-question-answering-models","title":"Mitigating Bias for Question Answering Models by Tracking Bias Influence","date":"2023-10-13","arxiv_id":"2310.08795","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalarization-for-multi-task-and-multi-domain","title":"Scalarization for Multi-Task and Multi-Domain Learning at Scale","date":"2023-10-13","arxiv_id":"2310.08910","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-deep-learning-framework-for-quality","title":"Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images","date":"2023-10-13","arxiv_id":"2310.08805","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-policy-recovery-modeling-and","title":"Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning","date":"2023-10-11","arxiv_id":"2310.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"heuristic-vision-pre-training-with-self","title":"Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning","date":"2023-10-11","arxiv_id":"2310.07510","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-enabled-automatic-vessel","title":"Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance","date":"2023-10-11","arxiv_id":"2310.07212","repositories_listed":0,"syntology":null},{"url":null,"slug":"vsanet-real-time-speech-enhancement-based-on","title":"VSANet: Real-time Speech Enhancement Based on Voice Activity Detection and Causal Spatial Attention","date":"2023-10-11","arxiv_id":"2310.07295","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-compatible-ldpc-neural-decoding-network","title":"Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach","date":"2023-10-10","arxiv_id":"2310.06256","repositories_listed":0,"syntology":null},{"url":"/paper/m3fpolypsegnet-segmentation-network-with","slug":"m3fpolypsegnet-segmentation-network-with","title":"M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images","date":"2023-10-09","arxiv_id":"2310.05538","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-facial-action-unit-detection-through","title":"Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction","date":"2023-10-08","arxiv_id":"2310.05207","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-multi-task-learning-for-base","title":"Attention-based Multi-task Learning for Base Editor Outcome Prediction","date":"2023-10-04","arxiv_id":"2310.02919","repositories_listed":0,"syntology":null},{"url":"/paper/universlu-universal-spoken-language","slug":"universlu-universal-spoken-language","title":"UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions","date":"2023-10-04","arxiv_id":"2310.02973","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuned-vs-prompt-tuned-supervised","title":"Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?","date":"2023-10-03","arxiv_id":"2310.01854","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-deepssm-training-methodology-for","title":"Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models","date":"2023-10-02","arxiv_id":"2310.01529","repositories_listed":0,"syntology":null},{"url":null,"slug":"finger-unet-a-u-net-based-multi-task","title":"Finger-UNet: A U-Net based Multi-Task Architecture for Deep Fingerprint Enhancement","date":"2023-10-01","arxiv_id":"2310.00629","repositories_listed":0,"syntology":null},{"url":null,"slug":"glioma-subtype-classification-from","title":"Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study","date":"2023-09-29","arxiv_id":"2309.17223","repositories_listed":0,"syntology":null},{"url":null,"slug":"distill-knowledge-in-multi-task-reinforcement","title":"Distill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization","date":"2023-09-27","arxiv_id":"2309.15603","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversion-of-single-energy-computed","title":"Conversion of single-energy computed tomography to parametric maps of dual-energy computed tomography using convolutional neural network","date":"2023-09-26","arxiv_id":"2309.15314","repositories_listed":0,"syntology":null},{"url":null,"slug":"sofari-high-dimensional-manifold-based","title":"SOFARI: High-Dimensional Manifold-Based Inference","date":"2023-09-26","arxiv_id":"2309.15032","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-lead-lidar-based-end-to-end-autonomous","title":"V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration","date":"2023-09-26","arxiv_id":"2309.15252","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-reduced-popularity","title":"Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations","date":"2023-09-25","arxiv_id":"2310.03148","repositories_listed":0,"syntology":null},{"url":null,"slug":"pi-rads-v2-compliant-automated-segmentation","title":"PI-RADS v2 Compliant Automated Segmentation of Prostate Zones Using co-training Motivated Multi-task Dual-Path CNN","date":"2023-09-22","arxiv_id":"2309.12970","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-real-time-multi-task-learning-system-for","title":"A Real-Time Multi-Task Learning System for Joint Detection of Face, Facial Landmark and Head Pose","date":"2023-09-21","arxiv_id":"2309.11773","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-cooperative-learning-via-searching","title":"Multi-Task Cooperative Learning via Searching for Flat Minima","date":"2023-09-21","arxiv_id":"2309.12090","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mutual-learning-across-task-towers-for","title":"Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning","date":"2023-09-19","arxiv_id":"2309.10357","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-heterogeneous-graph-based-multi-task","title":"A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid","date":"2023-09-18","arxiv_id":"2309.09921","repositories_listed":0,"syntology":null},{"url":null,"slug":"dealing-with-negative-samples-with-multi-task","title":"Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction","date":"2023-09-18","arxiv_id":"2309.09713","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-selection-and-assignment-for-multi-modal","title":"Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits","date":"2023-09-18","arxiv_id":"2309.09832","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-whisper-to-enhance-multi-branched","title":"Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata","date":"2023-09-18","arxiv_id":"2309.09548","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-stage-modality-distillation-for","title":"One-stage Modality Distillation for Incomplete Multimodal Learning","date":"2023-09-15","arxiv_id":"2309.08204","repositories_listed":0,"syntology":null},{"url":"/paper/rade-reference-assisted-dialogue-evaluation","slug":"rade-reference-assisted-dialogue-evaluation","title":"RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue","date":"2023-09-15","arxiv_id":"2309.08156","repositories_listed":0,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 1 unverified","sample_list":"/paper/rade-reference-assisted-dialogue-evaluation#ran","syntology_url":"https://syntology.ai/paper/2309.08156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.08156"}},"official":null}},{"url":null,"slug":"towards-word-level-end-to-end-neural-speaker","title":"Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network","date":"2023-09-15","arxiv_id":"2309.08489","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-learning-framework-for-drone","title":"A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction","date":"2023-09-13","arxiv_id":"2309.06741","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-auxiliary-sources-in","title":"Learning from Auxiliary Sources in Argumentative Revision Classification","date":"2023-09-13","arxiv_id":"2309.07334","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-multi-task-learning-for-3","title":"Getting More for Less: Using Weak Labels and AV-Mixup for Robust Audio-Visual Speaker Verification","date":"2023-09-13","arxiv_id":"2309.07115","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-multi-task-learning-framework-1","title":"Hierarchical Multi-Task Learning Framework for Session-based Recommendations","date":"2023-09-12","arxiv_id":"2309.06533","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-and-multi-task-learning-for","title":"Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection","date":"2023-09-12","arxiv_id":"2309.06288","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-specific-augmented-reality-guidance-for","title":"Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer","date":"2023-09-11","arxiv_id":"2309.05209","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-robot-learning-using-self","title":"Continual Robot Learning using Self-Supervised Task Inference","date":"2023-09-10","arxiv_id":"2309.04974","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-task-attention-network-improving-multi","title":"Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications","date":"2023-09-07","arxiv_id":"2309.03837","repositories_listed":0,"syntology":null},{"url":null,"slug":"myodex-a-generalizable-prior-for-dexterous","title":"MyoDex: A Generalizable Prior for Dexterous Manipulation","date":"2023-09-06","arxiv_id":"2309.03130","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-deep-learning-for-accurate-risk","title":"Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients","date":"2023-09-01","arxiv_id":"2309.00330","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-mandarin-prosodic-structure","title":"Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information","date":"2023-08-31","arxiv_id":"2308.16577","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-small-footprint-few-shot-keyword","title":"Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data","date":"2023-08-31","arxiv_id":"2309.00647","repositories_listed":0,"syntology":null},{"url":null,"slug":"alleviating-video-length-effect-for-micro","title":"Alleviating Video-Length Effect for Micro-video Recommendation","date":"2023-08-28","arxiv_id":"2308.14276","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-explainability-of-bi-rads","title":"Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation","date":"2023-08-27","arxiv_id":"2308.14213","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-scalarization-in-multi-task","title":"Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective","date":"2023-08-27","arxiv_id":"2308.13985","repositories_listed":0,"syntology":null},{"url":null,"slug":"attending-generalizability-in-course-of-deep","title":"Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning","date":"2023-08-25","arxiv_id":"2308.13503","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-budget-allocation-in-multi-task","title":"Label Budget Allocation in Multi-Task Learning","date":"2023-08-24","arxiv_id":"2308.12949","repositories_listed":0,"syntology":null},{"url":null,"slug":"multipa-a-multi-task-speech-pronunciation","title":"MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios","date":"2023-08-24","arxiv_id":"2308.12490","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-multi-resolution-communications","title":"Semantic Multi-Resolution Communications","date":"2023-08-22","arxiv_id":"2308.11604","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-pseudo-label-learning-for-non","title":"Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model","date":"2023-08-18","arxiv_id":"2308.09262","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-and-opportunities-of-using","title":"Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey","date":"2023-08-16","arxiv_id":"2308.08234","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-hypergraphs-for-learning","title":"Self-supervised Hypergraphs for Learning Multiple World Interpretations","date":"2023-08-15","arxiv_id":"2308.07615","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-receiver-task-oriented-communications","title":"Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning","date":"2023-08-14","arxiv_id":"2308.06884","repositories_listed":0,"syntology":null},{"url":null,"slug":"speechx-neural-codec-language-model-as-a","title":"SpeechX: Neural Codec Language Model as a Versatile Speech Transformer","date":"2023-08-14","arxiv_id":"2308.06873","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-deep-learning-meets-multi-task-learning","title":"When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation","date":"2023-08-14","arxiv_id":"2308.07093","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-task-specific-bottom-representation","title":"Deep Task-specific Bottom Representation Network for Multi-Task Recommendation","date":"2023-08-11","arxiv_id":"2308.05996","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformable-mixer-transformer-with-gating-for","title":"Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction","date":"2023-08-10","arxiv_id":"2308.05721","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-global-information-interaction","title":"Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation","date":"2023-08-10","arxiv_id":"2308.05274","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-out-of-distribution-dialect","title":"Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance","date":"2023-08-09","arxiv_id":"2308.04886","repositories_listed":0,"syntology":null},{"url":null,"slug":"brighten-and-colorize-a-decoupled-network-for","title":"Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement","date":"2023-08-06","arxiv_id":"2308.03029","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-task-interference-in-multi-task-1","title":"Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives","date":"2023-08-03","arxiv_id":"2308.02066","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-multi-task-learning-with-recursive","title":"Online Multi-Task Learning with Recursive Least Squares and Recursive Kernel Methods","date":"2023-08-03","arxiv_id":"2308.01938","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-classification","title":"Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans","date":"2023-08-02","arxiv_id":"2308.01137","repositories_listed":0,"syntology":null},{"url":null,"slug":"upb-at-iberlef-2023-autextification-detection","title":"UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles","date":"2023-08-02","arxiv_id":"2308.01408","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuller-unified-multi-modality-multi-task-3d","title":"FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration","date":"2023-07-31","arxiv_id":"2307.16617","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomy-aware-lymph-node-detection-in-chest","title":"Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification","date":"2023-07-28","arxiv_id":"2307.15271","repositories_listed":0,"syntology":null},{"url":null,"slug":"dephn-different-expression-parallel","title":"DEPHN: Different Expression Parallel Heterogeneous Network using virtual gradient optimization for Multi-task Learning","date":"2023-07-24","arxiv_id":"2307.12519","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-train-adapt-and-detect-multi-task-adapter","title":"Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection","date":"2023-07-20","arxiv_id":"2307.10685","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-orders-of-user-behaviors-via","title":"Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion","date":"2023-07-18","arxiv_id":"2307.09089","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multi-task-model-imitating","title":"A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images","date":"2023-07-17","arxiv_id":"2307.08308","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-bevmtn-real-time-lidar-bird-s-eye-view","title":"LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving","date":"2023-07-17","arxiv_id":"2307.08850","repositories_listed":0,"syntology":null},{"url":null,"slug":"raymvsnet-learning-ray-based-1d-implicit-1","title":"RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo","date":"2023-07-16","arxiv_id":"2307.10233","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-multi-task-representation-learning","title":"Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks","date":"2023-07-13","arxiv_id":"2307.06887","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-better-ranking-consistency-a","title":"Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking","date":"2023-07-12","arxiv_id":"2307.11096","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-and-challenges-in-meta-learning-a","title":"Advances and Challenges in Meta-Learning: A Technical Review","date":"2023-07-10","arxiv_id":"2307.04722","repositories_listed":0,"syntology":null}],"record_sha256":"0ec11dc748b09d6591d9b41f2bc62756e92ca5b609d6bb491b48427fd88146a4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}