{"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/96","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":96,"pages_in_order":104,"rows_per_page":100,"rows":[9501,9600],"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/95","next":"/task/decoder/papers/97","papers":[{"url":null,"slug":"updates-leak-data-set-inference-and","title":"Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning","date":"2019-04-01","arxiv_id":"1904.01067","repositories_listed":0,"syntology":null},{"url":null,"slug":"ee-ae-an-exclusivity-enhanced-unsupervised","title":"EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach","date":"2019-03-30","arxiv_id":"1904.00172","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-translation-considering-context","title":"Machine translation considering context information using Encoder-Decoder model","date":"2019-03-30","arxiv_id":"1904.00160","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convolutional-neural-network-for-language","title":"A Convolutional Neural Network for Language-Agnostic Source Code Summarization","date":"2019-03-29","arxiv_id":"1904.00805","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-training-framework-for-text-to-speech","title":"Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet","date":"2019-03-29","arxiv_id":"1903.12389","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-fusion-encoder-decoder-network-for","title":"Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation","date":"2019-03-28","arxiv_id":"1903.11834","repositories_listed":0,"syntology":null},{"url":null,"slug":"190410045","title":"Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition","date":"2019-03-27","arxiv_id":"1904.10045","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-diverse-high-resolution-images","title":"Generating Diverse High-Resolution Images with VQ-VAE","date":"2019-03-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilevel-text-normalization-with-sequence","title":"Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning","date":"2019-03-27","arxiv_id":"1903.11340","repositories_listed":0,"syntology":null},{"url":null,"slug":"w-net-reinforced-u-net-for-density-map","title":"W-Net: Reinforced U-Net for Density Map Estimation","date":"2019-03-27","arxiv_id":"1903.11249","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-diverse-text-generation-by-self","title":"Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder","date":"2019-03-26","arxiv_id":"1903.10842","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-text-style","title":"Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus","date":"2019-03-26","arxiv_id":"1903.10671","repositories_listed":0,"syntology":null},{"url":null,"slug":"unpaired-image-captioning-via-scene-graph","title":"Unpaired Image Captioning via Scene Graph Alignments","date":"2019-03-26","arxiv_id":"1903.10658","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-multi-modal-policy-via-imitating","title":"Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors","date":"2019-03-25","arxiv_id":"1903.10304","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-abstractive-text-summarization-and","title":"Neural Abstractive Text Summarization and Fake News Detection","date":"2019-03-24","arxiv_id":"1904.00788","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotated-feature-network-for-multi-orientation","title":"Rotated Feature Network for multi-orientation object detection","date":"2019-03-23","arxiv_id":"1903.09839","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2cnet-a-deep-learning-framework-to-translate","title":"V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation","date":"2019-03-23","arxiv_id":"1903.10869","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-broadcast-decoder-a-simple-1","title":"Spatial Broadcast Decoder: A Simple Architecture for Disentangled Representations in VAEs","date":"2019-03-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-anatomical-priors","title":"Deep Learning with Anatomical Priors: Imitating Enhanced Autoencoders in Latent Space for Improved Pelvic Bone Segmentation in MRI","date":"2019-03-21","arxiv_id":"1903.09263","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-reconstruction-of-hardi","title":"Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network","date":"2019-03-21","arxiv_id":"1903.09272","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-prediction-and-multi-camera-fusion","title":"Short-Term Prediction and Multi-Camera Fusion on Semantic Grids","date":"2019-03-21","arxiv_id":"1903.08960","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-discovery-of-geometry-aware","title":"Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation","date":"2019-03-21","arxiv_id":"1903.08839","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-monocular-disparity-estimation","title":"A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning","date":"2019-03-20","arxiv_id":"1903.08514","repositories_listed":0,"syntology":null},{"url":null,"slug":"part-based-approximations-for-morphological","title":"Part-based approximations for morphological operators using asymmetric auto-encoders","date":"2019-03-20","arxiv_id":"1904.00763","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-generation-at-scale-a-case","title":"Natural Language Generation at Scale: A Case Study for Open Domain Question Answering","date":"2019-03-19","arxiv_id":"1903.08097","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-sixth-sense-with-artificial-intelligence","title":"Through-Wall Pose Imaging in Real-Time with a Many-to-Many Encoder/Decoder Paradigm","date":"2019-03-15","arxiv_id":"1904.00739","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-imitating-visual-attention-of-experts","title":"Toward Imitating Visual Attention of Experts in Software Development Tasks","date":"2019-03-15","arxiv_id":"1903.06320","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-switch-networks-for-generating-discrete","title":"Deep Switch Networks for Generating Discrete Data and Language","date":"2019-03-14","arxiv_id":"1903.06135","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-symmetric-and-asymmetric","title":"Learning Symmetric and Asymmetric Steganography via Adversarial Training","date":"2019-03-13","arxiv_id":"1903.05297","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-shadow-removal-using-end-to-end-deep","title":"Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks","date":"2019-03-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-generative-model-of-speech-complex","title":"A Deep Generative Model of Speech Complex Spectrograms","date":"2019-03-08","arxiv_id":"1903.03269","repositories_listed":0,"syntology":null},{"url":null,"slug":"mix-and-match-networks-multi-domain-alignment","title":"Mix and match networks: cross-modal alignment for zero-pair image-to-image translation","date":"2019-03-08","arxiv_id":"1903.04294","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-cnn-based-multi-task-learning-for-open","title":"Deep CNN-based Multi-task Learning for Open-Set Recognition","date":"2019-03-07","arxiv_id":"1903.03161","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-semantic-segmentation-by-dense-fusion","title":"Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images","date":"2019-03-07","arxiv_id":"1903.02702","repositories_listed":0,"syntology":null},{"url":null,"slug":"dixit-interactive-visual-storytelling-via","title":"Dixit: Interactive Visual Storytelling via Term Manipulation","date":"2019-03-06","arxiv_id":"1903.02230","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-captioning-with-weakly-supervised","title":"Image captioning with weakly-supervised attention penalty","date":"2019-03-06","arxiv_id":"1903.02507","repositories_listed":0,"syntology":null},{"url":"/paper/decoders-matter-for-semantic-segmentation","slug":"decoders-matter-for-semantic-segmentation","title":"Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation","date":"2019-03-05","arxiv_id":"1903.02120","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-modeling-for-novelty-detection","title":"Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification","date":"2019-03-05","arxiv_id":"1903.01730","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-rank-preserving-hashing-for","title":"Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval","date":"2019-03-04","arxiv_id":"1903.01545","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibration-of-encoder-decoder-models-for","title":"Calibration of Encoder Decoder Models for Neural Machine Translation","date":"2019-03-03","arxiv_id":"1903.00802","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-and-density-estimation-by","title":"Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network","date":"2019-03-03","arxiv_id":"1903.00853","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-auto-decoder-neural-generative","title":"Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data","date":"2019-03-03","arxiv_id":"1903.00840","repositories_listed":0,"syntology":null},{"url":null,"slug":"extended-gaze-following-detecting-objects-in","title":"Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View","date":"2019-02-28","arxiv_id":"1902.10953","repositories_listed":0,"syntology":null},{"url":null,"slug":"gated-context-model-with-embedded-priors-for","title":"Gated Context Model with Embedded Priors for Deep Image Compression","date":"2019-02-27","arxiv_id":"1902.10480","repositories_listed":0,"syntology":null},{"url":"/paper/making-history-matter-gold-critic-sequence","slug":"making-history-matter-gold-critic-sequence","title":"Making History Matter: History-Advantage Sequence Training for Visual Dialog","date":"2019-02-25","arxiv_id":"1902.09326","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-deep-object-features-for-image","title":"Using Deep Object Features for Image Descriptions","date":"2019-02-25","arxiv_id":"1902.09969","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-logical-form-encodings-for-unsupervised","title":"EAT: a simple and versatile semantic representation format for multi-purpose NLP","date":"2019-02-25","arxiv_id":"1902.09381","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-autoregressive-machine-translation-with","title":"Non-Autoregressive Machine Translation with Auxiliary Regularization","date":"2019-02-22","arxiv_id":"1902.10245","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-conditional-generative-adversarial","title":"Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery","date":"2019-02-19","arxiv_id":"1902.06923","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-stereovision-based-3-d-scene","title":"Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality","date":"2019-02-17","arxiv_id":"1902.06255","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-based-dynamic-threshold","title":"Neural Network-Based Dynamic Threshold Detection for Non-Volatile Memories","date":"2019-02-17","arxiv_id":"1902.06289","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-using-deep","title":"Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks","date":"2019-02-17","arxiv_id":"1902.06328","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-generative-endmember-modeling-an","title":"Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing","date":"2019-02-14","arxiv_id":"1902.05528","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-vs-jpeg2000-image-compression-for","title":"GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation","date":"2019-02-12","arxiv_id":"1902.04311","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-authenticate-with-deep","title":"Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding","date":"2019-02-11","arxiv_id":"1902.04149","repositories_listed":0,"syntology":null},{"url":null,"slug":"architecture-compression","title":"Architecture Compression","date":"2019-02-08","arxiv_id":"1902.03326","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-encoder-decoder-learning-framework","title":"Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network","date":"2019-02-08","arxiv_id":"1902.05376","repositories_listed":0,"syntology":null},{"url":null,"slug":"size-independent-neural-transfer-for-rddl","title":"Size Independent Neural Transfer for RDDL Planning","date":"2019-02-08","arxiv_id":"1902.03081","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-neural-network-for","title":"Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy","date":"2019-02-07","arxiv_id":"1902.03122","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-anchored-speech-recognition","title":"End-to-end Anchored Speech Recognition","date":"2019-02-06","arxiv_id":"1902.02383","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-neuro-inspired-creative-decoder","title":"Toward A Neuro-inspired Creative Decoder","date":"2019-02-06","arxiv_id":"1902.02399","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-generative-retrieval-method-for","title":"An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain","date":"2019-02-02","arxiv_id":"1902.00592","repositories_listed":0,"syntology":null},{"url":"/paper/hierarchical-photo-scene-encoder-for-album","slug":"hierarchical-photo-scene-encoder-for-album","title":"Hierarchical Photo-Scene Encoder for Album Storytelling","date":"2019-02-02","arxiv_id":"1902.00669","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-classification-supervised-auto-encoder","title":"A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids","date":"2019-02-01","arxiv_id":"1902.00220","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-generating-long-and-coherent-text","title":"Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models","date":"2019-02-01","arxiv_id":"1902.00154","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-context-of-recurrent-neural","title":"Exploring the context of recurrent neural network based conversational agents","date":"2019-01-31","arxiv_id":"1901.11462","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-detector-for-ssds-in-the-presence","title":"Model-Based Detector for SSDs in the Presence of Inter-cell Interference","date":"2019-01-31","arxiv_id":"1902.01212","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimax-optimal-decoding-of-movement-goals","title":"Minimax-optimal decoding of movement goals from local field potentials using complex spectral features","date":"2019-01-29","arxiv_id":"1901.10397","repositories_listed":0,"syntology":null},{"url":null,"slug":"lie-group-auto-encoder","title":"Lie Group Auto-Encoder","date":"2019-01-28","arxiv_id":"1901.09970","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-encoder-decoders-with","title":"Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation","date":"2019-01-26","arxiv_id":"1901.09197","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-accuracy-with-quantified-privacy","title":"Better accuracy with quantified privacy: representations learned via reconstructive adversarial network","date":"2019-01-25","arxiv_id":"1901.08730","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-shape-learning-and-segmentation-for","title":"Joint shape learning and segmentation for medical images using a minimalistic deep network","date":"2019-01-25","arxiv_id":"1901.08824","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-representation-bottleneck-in","title":"Addressing the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts","date":"2019-01-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-belief-propagation-decoding-with","title":"Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation","date":"2019-01-24","arxiv_id":"1901.08621","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-geometry-of-encoder-decoder","title":"Understanding Geometry of Encoder-Decoder CNNs","date":"2019-01-22","arxiv_id":"1901.07647","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-decoder-for-topological-codes-using","title":"Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix","date":"2019-01-21","arxiv_id":"1901.07535","repositories_listed":0,"syntology":null},{"url":null,"slug":"polar-coded-distributed-matrix-multiplication","title":"Straggler Resilient Serverless Computing Based on Polar Codes","date":"2019-01-21","arxiv_id":"1901.06811","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-millimeter-wave-massive","title":"Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding","date":"2019-01-19","arxiv_id":"1901.06537","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-of-real-time-semantic-segmentation","title":"Design of Real-time Semantic Segmentation Decoder for Automated Driving","date":"2019-01-19","arxiv_id":"1901.06580","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-semi-supervised-variational","title":"Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction","date":"2019-01-18","arxiv_id":"1901.06103","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-mutually-local-global-u-nets-for","title":"Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images","date":"2019-01-18","arxiv_id":"1901.06047","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-latent-sentence-structure-in-neural","title":"Modeling Latent Sentence Structure in Neural Machine Translation","date":"2019-01-18","arxiv_id":"1901.06436","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascade-decoder-a-universal-decoding-method","title":"Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation","date":"2019-01-15","arxiv_id":"1901.04949","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-waveform-estimation-and","title":"Deep Learning for Waveform Estimation and Imaging in Passive Radar","date":"2019-01-14","arxiv_id":"1809.04768","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-joint-object-detection-and-semantic","title":"Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving","date":"2019-01-12","arxiv_id":"1901.03912","repositories_listed":0,"syntology":null},{"url":"/paper/preventing-posterior-collapse-with-delta-vaes","slug":"preventing-posterior-collapse-with-delta-vaes","title":"Preventing Posterior Collapse with delta-VAEs","date":"2019-01-10","arxiv_id":"1901.03416","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-and-turbo-iterations-for-mimo-receivers","title":"Self and turbo iterations for MIMO receivers and large-scale systems","date":"2019-01-10","arxiv_id":"1805.05065","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stream-cnn-based-video-semantic","title":"Multi-stream CNN based Video Semantic Segmentation for Automated Driving","date":"2019-01-08","arxiv_id":"1901.02511","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-motion-deblurring-with-cycle-generative","title":"Blind Motion Deblurring with Cycle Generative Adversarial Networks","date":"2019-01-07","arxiv_id":"1901.01641","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distance-map-regularized-cnn-for-cardiac","title":"A Distance Map Regularized CNN for Cardiac Cine MR Image Segmentation","date":"2019-01-04","arxiv_id":"1901.01238","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-of-partially-invertible","title":"Adaptive Density Estimation for Generative Models","date":"2019-01-04","arxiv_id":"1901.01091","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-graph-embedding-with-adversarial","title":"Learning Graph Embedding with Adversarial Training Methods","date":"2019-01-04","arxiv_id":"1901.01250","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-semantic-learning-strategy-for-layout","title":"Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment","date":"2019-01-03","arxiv_id":"1901.00621","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-acoustic-to-word-ctc-model-with","title":"Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units","date":"2018-12-31","arxiv_id":"1812.11928","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-frame-prediction-for-video-coding","title":"Deep Frame Prediction for Video Coding","date":"2018-12-31","arxiv_id":"1901.00062","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-performance-decoder-for-convolutional","title":"High-performance Decoder for Convolutional Code with Deep Neural Network","date":"2018-12-30","arxiv_id":"1812.11455","repositories_listed":0,"syntology":null},{"url":null,"slug":"smplr-deep-smpl-reverse-for-3d-human-pose-and","title":"SMPLR: Deep SMPL reverse for 3D human pose and shape recovery","date":"2018-12-27","arxiv_id":"1812.10766","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-refine-source-representations-for","title":"Learning to Refine Source Representations for Neural Machine Translation","date":"2018-12-26","arxiv_id":"1812.10230","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmfnet-a-multi-modality-mri-fusion-network","title":"MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma","date":"2018-12-25","arxiv_id":"1812.10033","repositories_listed":0,"syntology":null},{"url":null,"slug":"bci-decoder-performance-comparison-of-an-lstm","title":"BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation","date":"2018-12-24","arxiv_id":"1812.09835","repositories_listed":0,"syntology":null}],"record_sha256":"af082063af311e77863a8a65c9d3d364c77e7c78f8ab2f67da4056dc47e33c11","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}