{"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/74","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":74,"pages_in_order":104,"rows_per_page":100,"rows":[7301,7400],"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/73","next":"/task/decoder/papers/75","papers":[{"url":null,"slug":"multiauto-deeponet-a-multi-resolution","title":"MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems","date":"2022-04-07","arxiv_id":"2204.03193","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-instance-edge-detection","title":"End-to-End Instance Edge Detection","date":"2022-04-06","arxiv_id":"2204.02898","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-direct-speech-to-speech-translation","title":"Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation","date":"2022-04-06","arxiv_id":"2204.02967","repositories_listed":0,"syntology":null},{"url":null,"slug":"plutonet-an-efficient-polyp-segmentation","title":"PlutoNet: An Efficient Polyp Segmentation Network with Modified Partial Decoder and Decoder Consistency Training","date":"2022-04-06","arxiv_id":"2204.03652","repositories_listed":0,"syntology":null},{"url":null,"slug":"abstractive-summarization-of-hospitalisation","title":"Abstractive summarization of hospitalisation histories with transformer networks","date":"2022-04-05","arxiv_id":"2204.02208","repositories_listed":0,"syntology":null},{"url":null,"slug":"hear-no-evil-towards-adversarial-robustness","title":"Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning","date":"2022-04-05","arxiv_id":"2204.02381","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-voice-trigger-detection-with-metric","title":"Improving Voice Trigger Detection with Metric Learning","date":"2022-04-05","arxiv_id":"2204.02455","repositories_listed":0,"syntology":null},{"url":null,"slug":"latentgan-autoencoder-learning-disentangled","title":"LatentGAN Autoencoder: Learning Disentangled Latent Distribution","date":"2022-04-05","arxiv_id":"2204.02010","repositories_listed":0,"syntology":null},{"url":"/paper/non-local-latent-relation-distillation-for-1","slug":"non-local-latent-relation-distillation-for-1","title":"Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation","date":"2022-04-05","arxiv_id":"2204.01971","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-application-of-pixel-interval-down","title":"An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images","date":"2022-04-04","arxiv_id":"2204.01341","repositories_listed":0,"syntology":null},{"url":null,"slug":"deliberation-model-for-on-device-spoken","title":"Deliberation Model for On-Device Spoken Language Understanding","date":"2022-04-04","arxiv_id":"2204.01893","repositories_listed":0,"syntology":null},{"url":null,"slug":"fitting-an-immersed-submanifold-to-data-via","title":"Fitting an immersed submanifold to data via Sussmann's orbit theorem","date":"2022-04-03","arxiv_id":"2204.01119","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-factual-accuracy-of-abstractive","title":"Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization","date":"2022-04-02","arxiv_id":"2204.00797","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-to-look-at-and-where-semantic-and","title":"What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions","date":"2022-04-02","arxiv_id":"2204.00746","repositories_listed":0,"syntology":null},{"url":null,"slug":"filter-based-discriminative-autoencoders-for","title":"Filter-based Discriminative Autoencoders for Children Speech Recognition","date":"2022-04-01","arxiv_id":"2204.00164","repositories_listed":0,"syntology":null},{"url":null,"slug":"interaug-augmenting-noisy-intermediate","title":"InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR","date":"2022-04-01","arxiv_id":"2204.00174","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-rnn-t-with-semantic-decoder-for","title":"Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding","date":"2022-04-01","arxiv_id":"2204.00558","repositories_listed":0,"syntology":null},{"url":null,"slug":"nc-dre-leveraging-non-entity-clue-information","title":"NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction","date":"2022-04-01","arxiv_id":"2204.00255","repositories_listed":0,"syntology":null},{"url":null,"slug":"bangla-hate-speech-detection-on-social-media","title":"Bangla hate speech detection on social media using attention-based recurrent neural network","date":"2022-03-31","arxiv_id":"2203.16775","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-zero-shot-many-to-many-voice","title":"Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE","date":"2022-03-30","arxiv_id":"2203.16037","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-local-displacements-for-point-cloud","title":"Learning Local Displacements for Point Cloud Completion","date":"2022-03-30","arxiv_id":"2203.16600","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-speaker-diarization-with-dynamic","title":"Multi-scale Speaker Diarization with Dynamic Scale Weighting","date":"2022-03-30","arxiv_id":"2203.15974","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-model-for-burn-depth","title":"A deep learning model for burn depth classification using ultrasound imaging","date":"2022-03-29","arxiv_id":"2203.15879","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-reflectance-capture-with-a-deep","title":"Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts","date":"2022-03-29","arxiv_id":"2203.15258","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-routing-transformer-for-zero-shot","title":"Hybrid Routing Transformer for Zero-Shot Learning","date":"2022-03-29","arxiv_id":"2203.15310","repositories_listed":0,"syntology":null},{"url":null,"slug":"mstr-multi-scale-transformer-for-end-to-end","title":"MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection","date":"2022-03-28","arxiv_id":"2203.14709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spiking-neural-network-based-on-neural","title":"A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data","date":"2022-03-26","arxiv_id":"2204.05132","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-mocon-neural-motion-control-for","title":"Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture","date":"2022-03-26","arxiv_id":"2203.14065","repositories_listed":0,"syntology":null},{"url":null,"slug":"chain-based-discriminative-autoencoders-for","title":"Chain-based Discriminative Autoencoders for Speech Recognition","date":"2022-03-25","arxiv_id":"2203.13687","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-target-side-morphology-in-neural","title":"Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies","date":"2022-03-25","arxiv_id":"2203.13550","repositories_listed":0,"syntology":null},{"url":null,"slug":"rd-optimized-trit-plane-coding-of-deep","title":"RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors","date":"2022-03-25","arxiv_id":"2203.13467","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangleing-content-and-fine-grained","title":"Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion","date":"2022-03-24","arxiv_id":"2203.12813","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-motion-dependent-appearance-for-high","title":"Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera","date":"2022-03-24","arxiv_id":"2203.12780","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervision-through-random-segments-with","title":"Self-supervision through Random Segments with Autoregressive Coding (RandSAC)","date":"2022-03-22","arxiv_id":"2203.12054","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabart-a-pretrained-arabic-sequence-to-1","title":"AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization","date":"2022-03-21","arxiv_id":"2203.10945","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-duration-modeling-for-end-to","title":"AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling","date":"2022-03-21","arxiv_id":"2203.11049","repositories_listed":0,"syntology":null},{"url":null,"slug":"me-net-multi-encoder-net-framework-for-brain","title":"ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation","date":"2022-03-21","arxiv_id":"2203.11213","repositories_listed":0,"syntology":null},{"url":null,"slug":"upsampling-autoencoder-for-self-supervised","title":"Upsampling Autoencoder for Self-Supervised Point Cloud Learning","date":"2022-03-21","arxiv_id":"2203.10768","repositories_listed":0,"syntology":null},{"url":null,"slug":"wesinger-data-augmented-singing-voice","title":"WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses","date":"2022-03-21","arxiv_id":"2203.10750","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-encoder-decoder-architecture-for","title":"Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey","date":"2022-03-20","arxiv_id":"2203.10480","repositories_listed":0,"syntology":null},{"url":null,"slug":"font-generation-with-missing-impression","title":"Font Generation with Missing Impression Labels","date":"2022-03-19","arxiv_id":"2203.10348","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-domain-multi-definition-landmark","title":"Multi-Domain Multi-Definition Landmark Localization for Small Datasets","date":"2022-03-19","arxiv_id":"2203.10358","repositories_listed":0,"syntology":null},{"url":null,"slug":"completing-partial-point-clouds-with-outliers","title":"Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation","date":"2022-03-18","arxiv_id":"2203.09772","repositories_listed":0,"syntology":null},{"url":null,"slug":"laneformer-object-aware-row-column","title":"Laneformer: Object-aware Row-Column Transformers for Lane Detection","date":"2022-03-18","arxiv_id":"2203.09830","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-stage-deep-edge-detection-based-on-dense","title":"One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning","date":"2022-03-17","arxiv_id":"2203.09387","repositories_listed":0,"syntology":null},{"url":null,"slug":"planet-dynamic-content-planning-in","title":"PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation","date":"2022-03-17","arxiv_id":"2203.09100","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretr-spatio-temporal-non-autoregressive","title":"PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer","date":"2022-03-17","arxiv_id":"2203.09293","repositories_listed":0,"syntology":null},{"url":null,"slug":"ctlgan-few-shot-artistic-portraits-generation","title":"CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning","date":"2022-03-16","arxiv_id":"2203.08612","repositories_listed":0,"syntology":null},{"url":null,"slug":"cue-vectors-modular-training-of-language","title":"CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals","date":"2022-03-16","arxiv_id":"2203.08774","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-the-building-blocks-of-dark","title":"Discovering the building blocks of dark matter halo density profiles with neural networks","date":"2022-03-16","arxiv_id":"2203.08827","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-improving-sequence-to-1","title":"Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation","date":"2022-03-16","arxiv_id":"2203.08442","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-mix-example-interpolation","title":"Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation","date":"2022-03-15","arxiv_id":"2203.07627","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-rf-slam-for-unsupervised-positioning","title":"Neural RF SLAM for unsupervised positioning and mapping with channel state information","date":"2022-03-15","arxiv_id":"2203.08264","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-a-neural-network-hear-the-shape-of-a-drum","title":"Can A Neural Network Hear the Shape of A Drum?","date":"2022-03-14","arxiv_id":"2203.08073","repositories_listed":0,"syntology":null},{"url":null,"slug":"choose-your-qa-model-wisely-a-systematic","title":"Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering","date":"2022-03-14","arxiv_id":"2203.07522","repositories_listed":0,"syntology":null},{"url":null,"slug":"mdt-net-multi-domain-transfer-by-perceptual","title":"MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan","date":"2022-03-12","arxiv_id":"2203.06363","repositories_listed":0,"syntology":null},{"url":null,"slug":"babynet-reconstructing-3d-faces-of-babies","title":"BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs","date":"2022-03-11","arxiv_id":"2203.05908","repositories_listed":0,"syntology":null},{"url":null,"slug":"bit-metric-decoding-rate-in-multi-user-mimo-1","title":"Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory","date":"2022-03-11","arxiv_id":"2203.06271","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-domain-reconstruction-networks-with-v","title":"Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI","date":"2022-03-11","arxiv_id":"2203.05725","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-driven-versatile-video-coding-for","title":"Saliency-Driven Versatile Video Coding for Neural Object Detection","date":"2022-03-11","arxiv_id":"2203.05944","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-bi-directional-skip-connections-in","title":"Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond","date":"2022-03-11","arxiv_id":"2203.05709","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-streaming-asr-with","title":"Transformer-based Streaming ASR with Cumulative Attention","date":"2022-03-11","arxiv_id":"2203.05736","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-screen-shooting-resilient-document-image","title":"A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network","date":"2022-03-10","arxiv_id":"2203.05198","repositories_listed":0,"syntology":null},{"url":null,"slug":"compilable-neural-code-generation-with","title":"Compilable Neural Code Generation with Compiler Feedback","date":"2022-03-10","arxiv_id":"2203.05132","repositories_listed":0,"syntology":null},{"url":"/paper/deer-detection-agnostic-end-to-end-recognizer","slug":"deer-detection-agnostic-end-to-end-recognizer","title":"DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting","date":"2022-03-10","arxiv_id":"2203.05122","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enriched-attention-network-with","title":"Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling","date":"2022-03-10","arxiv_id":"2203.05346","repositories_listed":0,"syntology":null},{"url":null,"slug":"look-backward-and-forward-self-knowledge","title":"Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation","date":"2022-03-10","arxiv_id":"2203.05248","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-convolutional-transformer-with","title":"Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification","date":"2022-03-09","arxiv_id":"2203.04771","repositories_listed":0,"syntology":null},{"url":null,"slug":"bevsegformer-bird-s-eye-view-semantic","title":"BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs","date":"2022-03-08","arxiv_id":"2203.04050","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-branch-aggregation-network-for","title":"Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes","date":"2022-03-08","arxiv_id":"2203.04037","repositories_listed":0,"syntology":null},{"url":"/paper/lane-detection-with-versatile-atrousformer","slug":"lane-detection-with-versatile-atrousformer","title":"Lane Detection with Versatile AtrousFormer and Local Semantic Guidance","date":"2022-03-08","arxiv_id":"2203.04067","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-cognitive-speech-compression","title":"Practical cognitive speech compression","date":"2022-03-08","arxiv_id":"2203.04415","repositories_listed":0,"syntology":null},{"url":null,"slug":"stage-aware-feature-alignment-network-for","title":"Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes","date":"2022-03-08","arxiv_id":"2203.04031","repositories_listed":0,"syntology":null},{"url":null,"slug":"conquering-data-variations-in-resolution-a","title":"Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network","date":"2022-03-07","arxiv_id":"2203.03640","repositories_listed":0,"syntology":null},{"url":null,"slug":"singlesketch2mesh-generating-3d-mesh-model","title":"SingleSketch2Mesh : Generating 3D Mesh model from Sketch","date":"2022-03-07","arxiv_id":"2203.03157","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pre-trained-bert-for-audio","title":"Leveraging Pre-trained BERT for Audio Captioning","date":"2022-03-06","arxiv_id":"2203.02838","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-microphone-speaker-extraction-using","title":"Single microphone speaker extraction using unified time-frequency Siamese-Unet","date":"2022-03-06","arxiv_id":"2203.02941","repositories_listed":0,"syntology":null},{"url":null,"slug":"cptgraphsum-let-key-clues-guide-the-cross","title":"ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization","date":"2022-03-05","arxiv_id":"2203.02797","repositories_listed":0,"syntology":null},{"url":null,"slug":"style-erd-responsive-and-coherent-online","title":"Style-ERD: Responsive and Coherent Online Motion Style Transfer","date":"2022-03-04","arxiv_id":"2203.02574","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-neural-framework-for-image-caption","title":"A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism","date":"2022-03-03","arxiv_id":"2203.01594","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-latent-variable-models-for-text","title":"Deep Latent-Variable Models for Text Generation","date":"2022-03-03","arxiv_id":"2203.02055","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-hevc-decoding-energy-using-the","title":"Estimating the HEVC Decoding Energy Using the Decoder Processing Time","date":"2022-03-03","arxiv_id":"2203.01767","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-latent-variables-in-deep-state","title":"Interpretable Latent Variables in Deep State Space Models","date":"2022-03-03","arxiv_id":"2203.02057","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-optimization-of-rate-distortion-and","title":"Joint Optimization of Rate, Distortion, and Decoding Energy for HEVC Intraframe Coding","date":"2022-03-03","arxiv_id":"2203.01765","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-energy-consumption-of-hevc-intra","title":"Modeling the Energy Consumption of HEVC Intra Decoding","date":"2022-03-03","arxiv_id":"2203.01755","repositories_listed":0,"syntology":null},{"url":"/paper/aggregated-pyramid-vision-transformer-split","slug":"aggregated-pyramid-vision-transformer-split","title":"Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions","date":"2022-03-02","arxiv_id":"2203.00960","repositories_listed":0,"syntology":null},{"url":null,"slug":"d-2etr-decoder-only-detr-with-computationally","title":"D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention","date":"2022-03-02","arxiv_id":"2203.00860","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-linear-manifold-rom-with-convolutional","title":"Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method","date":"2022-03-01","arxiv_id":"2203.00360","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-based-bidirectional-global-context","title":"Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation","date":"2022-02-28","arxiv_id":"2202.13663","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-branched-spatio-temporal-fusion-network","title":"Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast","date":"2022-02-27","arxiv_id":"2202.13336","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-document-image-watermarking-scheme","title":"A Robust Document Image Watermarking Scheme using Deep Neural Network","date":"2022-02-26","arxiv_id":"2202.13067","repositories_listed":0,"syntology":null},{"url":null,"slug":"screening-gender-transfer-in-neural-machine-1","title":"Screening Gender Transfer in Neural Machine Translation","date":"2022-02-25","arxiv_id":"2202.12568","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-object-dynamics-with","title":"Learning Multi-Object Dynamics with Compositional Neural Radiance Fields","date":"2022-02-24","arxiv_id":"2202.11855","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-modelling-of-granger-causality","title":"Deep Recurrent Modelling of Granger Causality with Latent Confounding","date":"2022-02-23","arxiv_id":"2202.11286","repositories_listed":0,"syntology":null},{"url":null,"slug":"enabling-arbitrary-translation-objectives-1","title":"Enabling arbitrary translation objectives with Adaptive Tree Search","date":"2022-02-23","arxiv_id":"2202.11444","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-block-neural-architecture-search-for","title":"Mixed-Block Neural Architecture Search for Medical Image Segmentation","date":"2022-02-23","arxiv_id":"2202.11401","repositories_listed":0,"syntology":null},{"url":null,"slug":"nuclei-panoptic-segmentation-and-composition","title":"Nuclei panoptic segmentation and composition regression with multi-task deep neural networks","date":"2022-02-23","arxiv_id":"2202.11804","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-cluster-patterns-for-abstractive","title":"Learning Cluster Patterns for Abstractive Summarization","date":"2022-02-22","arxiv_id":"2202.10967","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-program-repair-systems-challenges-and","title":"Neural Program Repair: Systems, Challenges and Solutions","date":"2022-02-22","arxiv_id":"2202.10868","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-adversarial-perturbation-for-remote","title":"Universal adversarial perturbation for remote sensing images","date":"2022-02-22","arxiv_id":"2202.10693","repositories_listed":0,"syntology":null}],"record_sha256":"fd8ccc545b7e31732580cf5e0678fcbfbe3d437d34e4c6af95e60fb4ea0e7889","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}