{"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/decoder/papers/76","list_of":"/task/decoder","task":"Decoder","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":76,"pages_in_order":104,"rows_per_page":100,"rows":[7501,7600],"of":10368,"counts":{"archive_papers_tagged":10368,"with_a_code_link":4358,"where_syntology_ran_a_sample":1061,"not_listed_spam_title":0,"listed":10368,"listed_where_code_ran":1061,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":152,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":152,"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/decoder","prev":"/task/decoder/papers/75","next":"/task/decoder/papers/77","papers":[{"url":null,"slug":"blind-face-restoration-via-integrating-face","title":"Blind Face Restoration via Integrating Face Shape and Generative Priors","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"destr-object-detection-with-split-transformer","title":"DESTR: Object Detection With Split Transformer","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distillation-using-oracle-queries-for","title":"Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-scene-graph-generation-via","title":"Dynamic Scene Graph Generation via Anticipatory Pre-Training","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/likert-scoring-with-grade-decoupling-for-long","slug":"likert-scoring-with-grade-decoupling-for-long","title":"Likert Scoring With Grade Decoupling for Long-Term Action Assessment","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-window-fully-connected-crfs-for","title":"Neural Window Fully-Connected CRFs for Monocular Depth Estimation","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"patchtrack-multiple-object-tracking-using","title":"PatchTrack: Multiple Object Tracking Using Frame Patches","date":"2022-01-01","arxiv_id":"2201.00080","repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-simulation-layer-for-accurate-3d","title":"Physical Simulation Layer for Accurate 3D Modeling","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramid-architecture-for-multi-scale","title":"Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"salypath360-saliency-and-scanpath-prediction","title":"SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images","date":"2022-01-01","arxiv_id":"2201.00096","repositories_listed":0,"syntology":null},{"url":null,"slug":"seethroughnet-resurrection-of-auxiliary-loss","title":"SeeThroughNet: Resurrection of Auxiliary Loss by Preserving Class Probability Information","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spaceedit-learning-a-unified-editing-space-1","title":"SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-driven-tongue-animation","title":"Speech Driven Tongue Animation","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-matters-radiology-report-generation","title":"Knowledge Matters: Radiology Report Generation with General and Specific Knowledge","date":"2021-12-30","arxiv_id":"2112.15009","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-autoencoder-communications-with","title":"End-to-End Autoencoder Communications with Optimized Interference Suppression","date":"2021-12-29","arxiv_id":"2201.01388","repositories_listed":0,"syntology":null},{"url":null,"slug":"res2netfuse-a-fusion-method-for-infrared-and","title":"Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible Images","date":"2021-12-29","arxiv_id":"2112.14540","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compact-neural-network-based-algorithm-for","title":"A Compact Neural Network-based Algorithm for Robust Image Watermarking","date":"2021-12-27","arxiv_id":"2112.13491","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-dialect-arabic-speech-recognition","title":"Multi-Dialect Arabic Speech Recognition","date":"2021-12-25","arxiv_id":"2112.14678","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-ensembles-in-bioimage-segmentation","title":"Deep ensembles in bioimage segmentation","date":"2021-12-24","arxiv_id":"2112.12955","repositories_listed":0,"syntology":null},{"url":null,"slug":"drf-codes-deep-snr-robust-feedback-codes","title":"DRF Codes: Deep SNR-Robust Feedback Codes","date":"2021-12-22","arxiv_id":"2112.11789","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-shared-neural-manifolds-from-multi-1","title":"Learning shared neural manifolds from multi-subject FMRI data","date":"2021-12-22","arxiv_id":"2201.00622","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-neural-networks-for-error","title":"Adversarial Neural Networks for Error Correcting Codes","date":"2021-12-21","arxiv_id":"2112.11491","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-image-complexity-in-macro-level","title":"Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation","date":"2021-12-21","arxiv_id":"2112.11065","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-size-and-width-of-the-decoder-of-a","title":"On the Size and Width of the Decoder of a Boolean Threshold Autoencoder","date":"2021-12-21","arxiv_id":"2112.10933","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-attention-network-with-dense","title":"Contrastive Attention Network with Dense Field Estimation for Face Completion","date":"2021-12-20","arxiv_id":"2112.10310","repositories_listed":0,"syntology":null},{"url":null,"slug":"turbo-sim-a-generalised-generative-model-with","title":"Turbo-Sim: a generalised generative model with a physical latent space","date":"2021-12-20","arxiv_id":"2112.10629","repositories_listed":0,"syntology":null},{"url":null,"slug":"list-autoencoder-towards-deep-learning-based","title":"List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels","date":"2021-12-19","arxiv_id":"2112.11920","repositories_listed":0,"syntology":null},{"url":null,"slug":"gcn-geo-a-graph-convolution-network-based","title":"GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework","date":"2021-12-18","arxiv_id":"2112.10767","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearest-neighbor-search-with-compact-codes-a","title":"Nearest neighbor search with compact codes: A decoder perspective","date":"2021-12-17","arxiv_id":"2112.09568","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptation-and-attention-for-neural-video","title":"Adaptation and Attention for Neural Video Coding","date":"2021-12-16","arxiv_id":"2112.08767","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsgpt-domain-specific-generative-pre-training-1","title":"DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization","date":"2021-12-15","arxiv_id":"2112.08414","repositories_listed":0,"syntology":null},{"url":null,"slug":"coco-bert-improving-video-language-pre","title":"CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising","date":"2021-12-14","arxiv_id":"2112.07515","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-category-correlated-feature-for-few","title":"Exploring Category-correlated Feature for Few-shot Image Classification","date":"2021-12-14","arxiv_id":"2112.07224","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-hybrid-ctc-attention-end-to-end","title":"Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model","date":"2021-12-14","arxiv_id":"2112.07254","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-deblur-and-rotate-motion-blurred","title":"Learning to Deblur and Rotate Motion-Blurred Faces","date":"2021-12-14","arxiv_id":"2112.07599","repositories_listed":0,"syntology":null},{"url":null,"slug":"topnet-learning-from-neural-topic-model-to","title":"TopNet: Learning from Neural Topic Model to Generate Long Stories","date":"2021-12-14","arxiv_id":"2112.07259","repositories_listed":0,"syntology":null},{"url":null,"slug":"hformer-hybrid-cnn-transformer-for-fringe","title":"Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection","date":"2021-12-13","arxiv_id":"2112.06759","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-great-multi-lingual-teacher-with","title":"Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition","date":"2021-12-10","arxiv_id":"2112.05820","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-generative-models-using-weak","title":"Guided Generative Models using Weak Supervision for Detecting Object Spatial Arrangement in Overhead Images","date":"2021-12-10","arxiv_id":"2112.05786","repositories_listed":0,"syntology":null},{"url":null,"slug":"lipsound2-self-supervised-pre-training-for","title":"LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading","date":"2021-12-09","arxiv_id":"2112.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoder-based-communications-with","title":"Autoencoder-based Communications with Reconfigurable Intelligent Surfaces","date":"2021-12-08","arxiv_id":"2112.04441","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulslt-end-to-end-simultaneous-sign","title":"SimulSLT: End-to-End Simultaneous Sign Language Translation","date":"2021-12-08","arxiv_id":"2112.04228","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-robust-zero-shot-voice-conversion","title":"Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features","date":"2021-12-08","arxiv_id":"2112.04424","repositories_listed":0,"syntology":null},{"url":null,"slug":"cdgnet-a-cross-time-dynamic-graph-based-deep","title":"CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting","date":"2021-12-06","arxiv_id":"2112.02736","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-deep-hierarchical-variational","title":"Conditional Deep Hierarchical Variational Autoencoder for Voice Conversion","date":"2021-12-06","arxiv_id":"2112.02796","repositories_listed":0,"syntology":null},{"url":null,"slug":"doodleformer-creative-sketch-drawing-with","title":"DoodleFormer: Creative Sketch Drawing with Transformers","date":"2021-12-06","arxiv_id":"2112.03258","repositories_listed":0,"syntology":null},{"url":"/paper/u2-former-a-nested-u-shaped-transformer-for","slug":"u2-former-a-nested-u-shaped-transformer-for","title":"U2-Former: A Nested U-shaped Transformer for Image Restoration","date":"2021-12-04","arxiv_id":"2112.02279","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-feature-aggregation-module-for","title":"Localized Feature Aggregation Module for Semantic Segmentation","date":"2021-12-03","arxiv_id":"2112.01702","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-application-image-memes","title":"Multi-modal application: Image Memes Generation","date":"2021-12-03","arxiv_id":"2112.01651","repositories_listed":0,"syntology":null},{"url":"/paper/nonautoregressive-encoder-decoder-neural","slug":"nonautoregressive-encoder-decoder-neural","title":"Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction","date":"2021-12-03","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-occlusion-removal-for-hybrid","title":"Attention based Occlusion Removal for Hybrid Telepresence Systems","date":"2021-12-02","arxiv_id":"2112.01098","repositories_listed":0,"syntology":null},{"url":null,"slug":"co2sum-contrastive-learning-for-factual","title":"CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization","date":"2021-12-02","arxiv_id":"2112.01147","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generating-citation-sentences-for","title":"Towards Generating Citation Sentences for Multiple References with Intent Control","date":"2021-12-02","arxiv_id":"2112.01332","repositories_listed":0,"syntology":null},{"url":null,"slug":"unconstrained-face-sketch-synthesis-via","title":"Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark","date":"2021-12-02","arxiv_id":"2112.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scaled-encoder-decoder-network-for-image","title":"A Scaled Encoder Decoder Network for Image Captioning in Hindi","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-speech-recognition-for-the","title":"An End-to-End Speech Recognition for the Nepali Language","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-shallow-and-deep-representations","title":"Combining Shallow and Deep Representations for Text-Pair Classification","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-measurement-updates-for-bayes-filters","title":"Deep Measurement Updates for Bayes Filters","date":"2021-12-01","arxiv_id":"2112.00380","repositories_listed":0,"syntology":null},{"url":null,"slug":"encoder-decoder-approach-to-automated-essay","title":"Encoder Decoder Approach to Automated Essay Scoring For Deeper Semantic Analysis","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grin-generative-relation-and-intention","title":"GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-caption-generation-framework-for","title":"Image Caption Generation Framework for Assamese News using Attention Mechanism","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-segmentation-using-sparse","title":"Point Cloud Segmentation Using Sparse Temporal Local Attention","date":"2021-12-01","arxiv_id":"2112.00289","repositories_listed":0,"syntology":null},{"url":null,"slug":"stylistic-mr-to-text-generation-using-pre","title":"Stylistic MR-to-Text Generation Using Pre-trained Language Models","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"artseg-employing-attention-for-thermal-images","title":"ARTSeg: Employing Attention for Thermal images Semantic Segmentation","date":"2021-11-30","arxiv_id":"2111.15257","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-auto-encoder-with-neural-response","title":"Deep Auto-encoder with Neural Response","date":"2021-11-30","arxiv_id":"2111.15309","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-field-implicit-representation-for","title":"Light Field Implicit Representation for Flexible Resolution Reconstruction","date":"2021-11-30","arxiv_id":"2112.00185","repositories_listed":0,"syntology":null},{"url":null,"slug":"spaceedit-learning-a-unified-editing-space","title":"SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing","date":"2021-11-30","arxiv_id":"2112.00180","repositories_listed":0,"syntology":null},{"url":null,"slug":"vistra3-video-coding-with-deep-parameter","title":"ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing","date":"2021-11-30","arxiv_id":"2111.15536","repositories_listed":0,"syntology":null},{"url":null,"slug":"just-least-squares-binary-compressive","title":"Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension","date":"2021-11-29","arxiv_id":"2111.14486","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-attention-for-image-captioning-review","title":"Neural Attention for Image Captioning: Review of Outstanding Methods","date":"2021-11-29","arxiv_id":"2111.15015","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-antenna-testing-using-encoder","title":"Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection","date":"2021-11-27","arxiv_id":"2111.13884","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-pose-estimation-and-future-motion","title":"3D Pose Estimation and Future Motion Prediction from 2D Images","date":"2021-11-26","arxiv_id":"2111.13285","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyclic-graph-attentive-match-encoder-cgame-a","title":"Cyclic Graph Attentive Match Encoder (CGAME): A Novel Neural Network For OD Estimation","date":"2021-11-26","arxiv_id":"2111.14625","repositories_listed":0,"syntology":null},{"url":"/paper/morphology-decoder-a-machine-learning-guided","slug":"morphology-decoder-a-machine-learning-guided","title":"Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions","date":"2021-11-26","arxiv_id":"2111.13460","repositories_listed":0,"syntology":null},{"url":null,"slug":"surfemb-dense-and-continuous-correspondence","title":"SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings","date":"2021-11-26","arxiv_id":"2111.13489","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-is-more-generating-grounded-navigation","title":"Less is More: Generating Grounded Navigation Instructions from Landmarks","date":"2021-11-25","arxiv_id":"2111.12872","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-approaches-to-long-document-summarization","title":"New Approaches to Long Document Summarization: Fourier Transform Based Attention in a Transformer Model","date":"2021-11-25","arxiv_id":"2111.15473","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-parametric-data-augmentations-improve","title":"Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation","date":"2021-11-25","arxiv_id":"2111.12991","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-equivariant-3d-hand-mesh-generation","title":"Rotation Equivariant 3D Hand Mesh Generation from a Single RGB Image","date":"2021-11-25","arxiv_id":"2111.13023","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-korean-pretrained-language","title":"Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress","date":"2021-11-25","arxiv_id":"2112.03014","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-visual-semantic-feature-learning","title":"Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognition","date":"2021-11-24","arxiv_id":"2111.12351","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-resource-rich-language-datasets-for","title":"Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages","date":"2021-11-24","arxiv_id":"2111.12276","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-contextual-latent-space-model-subsequence","title":"A Contextual Latent Space Model: Subsequence Modulation in Melodic Sequence","date":"2021-11-23","arxiv_id":"2111.11703","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-video-decoding-for-vvc-using","title":"Energy Efficient Video Decoding for VVC Using a Greedy Strategy Based Design Space Exploration","date":"2021-11-23","arxiv_id":"2111.12194","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlvgen-a-dual-latent-variable-approach-to","title":"DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation","date":"2021-11-22","arxiv_id":"2111.11363","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchy-decoder-is-all-you-need-to-text","title":"Hierarchical Text Classification As Sub-Hierarchy Sequence Generation","date":"2021-11-22","arxiv_id":"2111.11104","repositories_listed":0,"syntology":null},{"url":"/paper/mail-a-unified-mask-image-language-trimodal","slug":"mail-a-unified-mask-image-language-trimodal","title":"MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation","date":"2021-11-21","arxiv_id":"2111.10747","repositories_listed":0,"syntology":null},{"url":null,"slug":"lattention-lattice-attention-in-asr-rescoring","title":"Lattention: Lattice-attention in ASR rescoring","date":"2021-11-19","arxiv_id":"2111.10157","repositories_listed":0,"syntology":null},{"url":null,"slug":"prosodic-clustering-for-phoneme-level-prosody","title":"Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis","date":"2021-11-19","arxiv_id":"2111.10177","repositories_listed":0,"syntology":null},{"url":null,"slug":"resistance-time-co-modulated-pointnet-for","title":"Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows","date":"2021-11-19","arxiv_id":"2111.10372","repositories_listed":0,"syntology":null},{"url":null,"slug":"csi-clustering-with-variational-autoencoding","title":"CSI Clustering with Variational Autoencoding","date":"2021-11-18","arxiv_id":"2111.09758","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-quality-streaming-speech-synthesis-with","title":"High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency","date":"2021-11-17","arxiv_id":"2111.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-computation-quantization-rcq-a","title":"Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding","date":"2021-11-17","arxiv_id":"2111.08920","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-to-sequence-model-for-joint-intent","title":"A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-model-agnostic-data-manipulation-method-for","title":"A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-end-to-end-capt-system-for-l2","title":"A Novel End-to-End CAPT System for L2 Children Learners","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-relation-attentive-3d-matrix-framework-for","title":"A Relation-Attentive 3D Matrix Framework for Relational Triple Extraction","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-zero-resource-approach-to-cross-lingual","title":"A Zero-Resource Approach to Cross-Lingual Query-Focused Abstractive Summarization","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-got-a-date-introducing-transformers-to-1","title":"BERT got a Date: Introducing Transformers to Temporal Tagging","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogved-a-pre-trained-latent-variable","title":"DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"ba1183b810a4df1f747c37d39f3b6998d3ef6f3ba0d54e3f80df6a3170062cc7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}