{"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/95","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":95,"pages_in_order":104,"rows_per_page":100,"rows":[9401,9500],"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/94","next":"/task/decoder/papers/96","papers":[{"url":null,"slug":"190600114","title":"Examining Structure of Word Embeddings with PCA","date":"2019-05-31","arxiv_id":"1906.00114","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-word-based-sentence-decoding-and","title":"Content Word-based Sentence Decoding and Evaluating for Open-domain Neural Response Generation","date":"2019-05-31","arxiv_id":"1905.13438","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-writing-style-imitation-via","title":"Effective writing style imitation via combinatorial paraphrasing","date":"2019-05-31","arxiv_id":"1905.13464","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-clouds-learning-with-attention-based","title":"Point Clouds Learning with Attention-based Graph Convolution Networks","date":"2019-05-31","arxiv_id":"1905.13445","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approach-for-receipt","title":"Deep Learning Approach for Receipt Recognition","date":"2019-05-30","arxiv_id":"1905.12817","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbabels-submission-to-the-wmt2019-ape-shared","title":"Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing","date":"2019-05-30","arxiv_id":"1905.13068","repositories_listed":0,"syntology":null},{"url":null,"slug":"190601496","title":"Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains","date":"2019-05-29","arxiv_id":"1906.01496","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimension-reduction-approach-for","title":"Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks","date":"2019-05-29","arxiv_id":"1905.12176","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-dilated-convolutional-nets-for-the","title":"Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels","date":"2019-05-28","arxiv_id":"1905.12120","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-symmetric-encoder-decoder-with-residual","title":"A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion","date":"2019-05-27","arxiv_id":"1905.11447","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-image-compression-post","title":"Attention Based Image Compression Post-Processing Convolutional Neural Network","date":"2019-05-27","arxiv_id":"1905.11045","repositories_listed":0,"syntology":null},{"url":null,"slug":"relational-representation-learning-for","title":"Representation Learning for Dynamic Graphs: A Survey","date":"2019-05-27","arxiv_id":"1905.11485","repositories_listed":0,"syntology":null},{"url":null,"slug":"190511559","title":"Road Segmentation with Image-LiDAR Data Fusion","date":"2019-05-26","arxiv_id":"1905.11559","repositories_listed":0,"syntology":null},{"url":null,"slug":"190513020","title":"Visualization of AE's Training on Credit Card Transactions with Persistent Homology","date":"2019-05-24","arxiv_id":"1905.13020","repositories_listed":0,"syntology":null},{"url":null,"slug":"cass-cross-adversarial-source-separation-via","title":"CASS: Cross Adversarial Source Separation via Autoencoder","date":"2019-05-23","arxiv_id":"1905.09877","repositories_listed":0,"syntology":null},{"url":"/paper/learning-fully-dense-neural-networks-for","slug":"learning-fully-dense-neural-networks-for","title":"Learning Fully Dense Neural Networks for Image Semantic Segmentation","date":"2019-05-22","arxiv_id":"1905.08929","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-sampling-network-for-fast-scene","title":"Spatial Sampling Network for Fast Scene Understanding","date":"2019-05-22","arxiv_id":"1905.09033","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-seq-to-seq-transformer-premised-temporal","title":"A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation","date":"2019-05-21","arxiv_id":"1905.08454","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-logical-forms-from-graph","title":"Generating Logical Forms from Graph Representations of Text and Entities","date":"2019-05-21","arxiv_id":"1905.08407","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-based-on-deep-learning","title":"Image Captioning based on Deep Learning Methods: A Survey","date":"2019-05-20","arxiv_id":"1905.08110","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-transformer-with-multi-view-visual","title":"Multimodal Transformer with Multi-View Visual Representation for Image Captioning","date":"2019-05-20","arxiv_id":"1905.07841","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-normalization-in-convolutional-neural-1","title":"Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients","date":"2019-05-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chive-varying-prosody-in-speech-synthesis","title":"CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network","date":"2019-05-17","arxiv_id":"1905.07195","repositories_listed":0,"syntology":null},{"url":null,"slug":"dueling-decoders-regularizing-variational","title":"Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces","date":"2019-05-17","arxiv_id":"1905.07478","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-dual-decoder-model-for-generating","title":"A Simple Dual-decoder Model for Generating Response with Sentiment","date":"2019-05-16","arxiv_id":"1905.06597","repositories_listed":0,"syntology":null},{"url":null,"slug":"embeddings-and-representation-learning-for","title":"Embeddings and Representation Learning for Structured Data","date":"2019-05-15","arxiv_id":"1905.06147","repositories_listed":0,"syntology":null},{"url":null,"slug":"atom-responding-machine-for-dialog-generation","title":"Atom Responding Machine for Dialog Generation","date":"2019-05-14","arxiv_id":"1905.05532","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-recognition-system-for-recognizing","title":"End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting","date":"2019-05-14","arxiv_id":"1905.05381","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-modeling-and-inverse-imaging-of","title":"Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential","date":"2019-05-12","arxiv_id":"1905.04803","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-unsupervised-learning-of-3d-point-clouds","title":"Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering","date":"2019-05-11","arxiv_id":"1905.04571","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-weight-updates-for-image","title":"Compressing Weight-updates for Image Artifacts Removal Neural Networks","date":"2019-05-10","arxiv_id":"1905.04079","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-modeling-with-deep-transformers","title":"Language Modeling with Deep Transformers","date":"2019-05-10","arxiv_id":"1905.04226","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-net-encoder-decoder-in-encoder-decoder","title":"T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography","date":"2019-05-10","arxiv_id":"1905.04197","repositories_listed":0,"syntology":null},{"url":"/paper/190503678","slug":"190503678","title":"What Do Single-view 3D Reconstruction Networks Learn?","date":"2019-05-09","arxiv_id":"1905.03678","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hardware-oriented-and-memory-efficient","title":"A Hardware-Oriented and Memory-Efficient Method for CTC Decoding","date":"2019-05-08","arxiv_id":"1905.03175","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-semantic-attention-network-for","title":"Multimodal Semantic Attention Network for Video Captioning","date":"2019-05-08","arxiv_id":"1905.02963","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-considering","title":"Convolutional Neural Networks Considering Local and Global features for Image Enhancement","date":"2019-05-07","arxiv_id":"1905.02899","repositories_listed":0,"syntology":null},{"url":null,"slug":"localizing-adverts-in-outdoor-scenes","title":"Localizing Adverts in Outdoor Scenes","date":"2019-05-06","arxiv_id":"1905.02106","repositories_listed":0,"syntology":null},{"url":null,"slug":"back-to-the-future-predicting-traffic","title":"Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network","date":"2019-05-04","arxiv_id":"1905.02197","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-quality-enhancement-via-multi-frame","title":"Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications","date":"2019-05-03","arxiv_id":"1905.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfa-scannet-pyramidal-feature-aggregation","title":"PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis","date":"2019-05-03","arxiv_id":"1905.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"delibgan-coarse-to-fine-text-generation-via","title":"DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/discourse-representation-structure-parsing-1","slug":"discourse-representation-structure-parsing-1","title":"Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"execution-guided-neural-program-synthesis","title":"Execution-Guided Neural Program Synthesis","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-maximization-auto-encoding","title":"INFORMATION MAXIMIZATION AUTO-ENCODING","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multimodal-graph-to-graph","title":"Learning Multimodal Graph-to-Graph Translation for Molecule Optimization","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-encoder-decoder-architectures-for","title":"Multi-level Encoder-Decoder Architectures for Image Restoration","date":"2019-05-01","arxiv_id":"1905.00322","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-learning-for-semantic-parsing-with","title":"Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-encoder-decoders-net-for-lane","title":"Multiple Encoder-Decoders Net for Lane Detection","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-tts-stylization-with-adversarial-and","title":"Neural TTS Stylization with Adversarial and Collaborative Games","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-semantic-embedding","title":"Probabilistic Semantic Embedding","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"question-generation-using-a-scratchpad","title":"Question Generation using a Scratchpad Encoder","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"riemannian-stochastic-gradient-descent-for","title":"Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"total-style-transfer-with-a-single-feed","title":"Total Style Transfer with a Single Feed-Forward Network","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-of-natural-visual-scenes-from","title":"Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks","date":"2019-04-30","arxiv_id":"1904.13007","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-deep-learning-models-for","title":"Sequence to sequence deep learning models for solar irradiation forecasting","date":"2019-04-30","arxiv_id":"1904.13081","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-coherent-and-engaging-spoken-dialog","title":"Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators","date":"2019-04-30","arxiv_id":"1904.13015","repositories_listed":0,"syntology":null},{"url":null,"slug":"190501998","title":"A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework","date":"2019-04-29","arxiv_id":"1905.01998","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-learning-in-deep-neural-networks","title":"Curriculum Learning in Deep Neural Networks for Financial Forecasting","date":"2019-04-29","arxiv_id":"1904.12887","repositories_listed":0,"syntology":null},{"url":null,"slug":"190501996","title":"Neural Machine Translation with Recurrent Highway Networks","date":"2019-04-28","arxiv_id":"1905.01996","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointing-novel-objects-in-image-captioning","title":"Pointing Novel Objects in Image Captioning","date":"2019-04-25","arxiv_id":"1904.11251","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-tolerance-of-neural-machine","title":"Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors","date":"2019-04-24","arxiv_id":"1904.10997","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmenting-the-future","title":"Segmenting the Future","date":"2019-04-24","arxiv_id":"1904.10666","repositories_listed":0,"syntology":null},{"url":null,"slug":"late-or-earlier-information-fusion-from-depth","title":"Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN","date":"2019-04-22","arxiv_id":"1904.09935","repositories_listed":0,"syntology":null},{"url":null,"slug":"ddnet-cartesian-polar-dual-domain-network-for","title":"DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation","date":"2019-04-18","arxiv_id":"1904.08773","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-collocate-neural-modules-for","title":"Learning to Collocate Neural Modules for Image Captioning","date":"2019-04-18","arxiv_id":"1904.08608","repositories_listed":0,"syntology":null},{"url":null,"slug":"tts-skins-speaker-conversion-via-asr","title":"TTS Skins: Speaker Conversion via ASR","date":"2019-04-18","arxiv_id":"1904.08983","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-investigation-for-chess-player","title":"Deep learning investigation for chess player attention prediction using eye-tracking and game data","date":"2019-04-17","arxiv_id":"1904.08155","repositories_listed":0,"syntology":null},{"url":null,"slug":"denet-a-universal-network-for-counting-crowd","title":"DENet: A Universal Network for Counting Crowd with Varying Densities and Scales","date":"2019-04-17","arxiv_id":"1904.08056","repositories_listed":0,"syntology":null},{"url":null,"slug":"guiding-ctc-posterior-spike-timings-for","title":"Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation","date":"2019-04-17","arxiv_id":"1904.08311","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-shape-synthesis-for-conceptual-design-and","title":"3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders","date":"2019-04-16","arxiv_id":"1904.07964","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-acoustic-unit-discovery-for","title":"Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks","date":"2019-04-16","arxiv_id":"1904.07556","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-text-comprehension-through","title":"Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation","date":"2019-04-15","arxiv_id":"1904.07142","repositories_listed":0,"syntology":null},{"url":null,"slug":"tdmr-detection-system-with-local-area","title":"TDMR Detection System with Local Area Influence Probabilistic a Priori Detector","date":"2019-04-13","arxiv_id":"1904.06599","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-singing-voice-conversion","title":"Unsupervised Singing Voice Conversion","date":"2019-04-13","arxiv_id":"1904.06590","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-mixed-lingual-neural-tts-system","title":"Building a mixed-lingual neural TTS system with only monolingual data","date":"2019-04-12","arxiv_id":"1904.06063","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-the-mapping-functions-of-denoising","title":"Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation","date":"2019-04-12","arxiv_id":"1904.06157","repositories_listed":0,"syntology":null},{"url":null,"slug":"ftgan-a-fully-trained-generative-adversarial","title":"FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation","date":"2019-04-11","arxiv_id":"1904.05729","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-locally-encoder-decoder-convolutional","title":"Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion","date":"2019-04-11","arxiv_id":"1904.05493","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-cross-lingual-transfer-of-neural","title":"Scalable Cross-Lingual Transfer of Neural Sentence Embeddings","date":"2019-04-11","arxiv_id":"1904.05542","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-novel-views-using-generative","title":"Predicting Novel Views Using Generative Adversarial Query Network","date":"2019-04-10","arxiv_id":"1904.05124","repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-gan-a-step-towards-parallel-text","title":"Bilingual-GAN: A Step Towards Parallel Text Generation","date":"2019-04-09","arxiv_id":"1904.04742","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossmodal-voice-conversion","title":"Crossmodal Voice Conversion","date":"2019-04-09","arxiv_id":"1904.04540","repositories_listed":0,"syntology":null},{"url":null,"slug":"embryo-staging-with-weakly-supervised-region","title":"Embryo staging with weakly-supervised region selection and dynamically-decoded predictions","date":"2019-04-09","arxiv_id":"1904.04419","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-monitoring-for-end-to-end-speech","title":"Performance Monitoring for End-to-End Speech Recognition","date":"2019-04-09","arxiv_id":"1904.04896","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-generation-with-exemplar-based-adaptive","title":"Text Generation with Exemplar-based Adaptive Decoding","date":"2019-04-09","arxiv_id":"1904.04428","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-domain-adaptation-translation-with","title":"Improving Domain Adaptation Translation with Domain Invariant and Specific Information","date":"2019-04-08","arxiv_id":"1904.03879","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-speaker-embeddings","title":"Self-supervised speaker embeddings","date":"2019-04-06","arxiv_id":"1904.03486","repositories_listed":0,"syntology":null},{"url":"/paper/token-level-ensemble-distillation-for","slug":"token-level-ensemble-distillation-for","title":"Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion","date":"2019-04-06","arxiv_id":"1904.03446","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-predictive-video-compression-with-bi","title":"Deep Predictive Video Compression with Bi-directional Prediction","date":"2019-04-05","arxiv_id":"1904.02909","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-baseline-for-video-captioning","title":"End-to-End Video Captioning","date":"2019-04-04","arxiv_id":"1904.02628","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-speech-recognition-with","title":"Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions","date":"2019-04-04","arxiv_id":"1904.02619","repositories_listed":0,"syntology":null},{"url":null,"slug":"uu-nets-connecting-discriminator-and","title":"UU-Nets Connecting Discriminator and Generator for Image to Image Translation","date":"2019-04-04","arxiv_id":"1904.02675","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-bundle-list-recommendation","title":"Personalized Bundle List Recommendation","date":"2019-04-03","arxiv_id":"1904.01933","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantics-aware-image-to-image-translation","title":"Semantics-Aware Image to Image Translation and Domain Transfer","date":"2019-04-03","arxiv_id":"1904.02203","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-semantic-guided-network-for-zero-shot","title":"Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval","date":"2019-04-03","arxiv_id":"1904.01971","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-segmentation-with-weak-supervision","title":"Towards annotation-efficient segmentation via image-to-image translation","date":"2019-04-02","arxiv_id":"1904.01636","repositories_listed":0,"syntology":null},{"url":null,"slug":"c2ae-class-conditioned-auto-encoder-for-open","title":"C2AE: Class Conditioned Auto-Encoder for Open-set Recognition","date":"2019-04-02","arxiv_id":"1904.01198","repositories_listed":0,"syntology":null},{"url":null,"slug":"lessons-from-building-acoustic-models-with-a","title":"Lessons from Building Acoustic Models with a Million Hours of Speech","date":"2019-04-02","arxiv_id":"1904.01624","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-gan-based-fault-diagnosis-approach","title":"A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series","date":"2019-04-01","arxiv_id":"1904.00575","repositories_listed":0,"syntology":null}],"record_sha256":"77cde3ac85beb2005ba4162067e15cc897deaab14042e2434a88f4c658a04e0f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}