{"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":"/method/sigmoid-activation/papers/65","list_of":"/method/sigmoid-activation","method":"Sigmoid Activation","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":65,"pages_in_order":72,"rows_per_page":100,"rows":[6401,6500],"of":7112,"counts":{"archive_papers_tagged":7112,"with_a_code_link":2470,"where_syntology_ran_a_sample":461,"not_listed_spam_title":0,"listed":7112,"listed_where_code_ran":461,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":386,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":386,"listed_every_run_a_failure_of_syntologys_instrument":75,"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":"/method/sigmoid-activation","prev":"/method/sigmoid-activation/papers/64","next":"/method/sigmoid-activation/papers/66","papers":[{"paper":null,"slug":"natural-language-statistical-features-of-lstm","title":"Natural Language Statistical Features of LSTM-generated Texts","date":"2018-04-10","arxiv_id":"1804.04087","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-intra-user-and-inter-user","title":"Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection","date":"2018-04-09","arxiv_id":"1804.03124","n_code_links":0,"syntology":null},{"paper":"/paper/recovering-realistic-texture-in-image-super","slug":"recovering-realistic-texture-in-image-super","title":"Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform","date":"2018-04-09","arxiv_id":"1804.02815","n_code_links":4,"syntology":null},{"paper":null,"slug":"scalable-sentiment-for-sequence-to-sequence","title":"Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis","date":"2018-04-07","arxiv_id":"1804.02504","n_code_links":0,"syntology":null},{"paper":"/paper/mix-and-match-networks-encoder-decoder","slug":"mix-and-match-networks-encoder-decoder","title":"Mix and match networks: encoder-decoder alignment for zero-pair image translation","date":"2018-04-06","arxiv_id":"1804.02199","n_code_links":1,"syntology":null},{"paper":"/paper/graph2seq-graph-to-sequence-learning-with","slug":"graph2seq-graph-to-sequence-learning-with","title":"Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks","date":"2018-04-03","arxiv_id":"1804.00823","n_code_links":4,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["IBM/Graph2Seq"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-spatiotemporal-models-for-robust","title":"Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification","date":"2018-04-02","arxiv_id":"1804.00736","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-quality-nonparallel-voice-conversion","title":"High-quality nonparallel voice conversion based on cycle-consistent adversarial network","date":"2018-04-02","arxiv_id":"1804.00425","n_code_links":0,"syntology":null},{"paper":"/paper/learning-latent-opinions-for-aspect-level","slug":"learning-latent-opinions-for-aspect-level","title":"Learning Latent Opinions for Aspect-Level Sentiment Classification","date":"2018-04-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/targeted-aspect-based-sentiment-analysis-via","slug":"targeted-aspect-based-sentiment-analysis-via","title":"Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM","date":"2018-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ddrprog-a-clevr-differentiable-dynamic","slug":"ddrprog-a-clevr-differentiable-dynamic","title":"DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer","date":"2018-03-30","arxiv_id":"1803.11361","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-stream-parallelization-of-recurrent","title":"Single Stream Parallelization of Recurrent Neural Networks for Low Power and Fast Inference","date":"2018-03-30","arxiv_id":"1803.11389","n_code_links":0,"syntology":null},{"paper":"/paper/attention-based-end-to-end-models-for-small","slug":"attention-based-end-to-end-models-for-small","title":"Attention-based End-to-End Models for Small-Footprint Keyword Spotting","date":"2018-03-29","arxiv_id":"1803.10916","n_code_links":3,"syntology":null},{"paper":null,"slug":"structured-weight-matrices-based-hardware","title":"Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs","date":"2018-03-28","arxiv_id":"1804.11239","n_code_links":0,"syntology":null},{"paper":"/paper/building-state-of-the-art-distant-speech","slug":"building-state-of-the-art-distant-speech","title":"Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline","date":"2018-03-27","arxiv_id":"1803.10109","n_code_links":0,"syntology":null},{"paper":"/paper/investigating-generative-adversarial-networks","slug":"investigating-generative-adversarial-networks","title":"Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition","date":"2018-03-27","arxiv_id":"1803.10132","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-talking-face-landmarks-from-speech","title":"Generating Talking Face Landmarks from Speech","date":"2018-03-26","arxiv_id":"1803.09803","n_code_links":0,"syntology":null},{"paper":"/paper/long-short-term-memory-and-learning-to-learn","slug":"long-short-term-memory-and-learning-to-learn","title":"Long short-term memory and learning-to-learn in networks of spiking neurons","date":"2018-03-26","arxiv_id":"1803.09574","n_code_links":2,"syntology":{"ran":3,"of":5,"n_ran_checked":0,"n_instrument":3,"unverified":2,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/towards-end-to-end-prosody-transfer-for","slug":"towards-end-to-end-prosody-transfer-for","title":"Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron","date":"2018-03-24","arxiv_id":"1803.09047","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/style-tokens-unsupervised-style-modeling","slug":"style-tokens-unsupervised-style-modeling","title":"Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis","date":"2018-03-23","arxiv_id":"1803.09017","n_code_links":11,"syntology":{"ran":19,"of":21,"n_ran_checked":13,"n_instrument":6,"unverified":2,"pointer_only":9,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/attention-learn-to-solve-routing-problems","slug":"attention-learn-to-solve-routing-problems","title":"Attention, Learn to Solve Routing Problems!","date":"2018-03-22","arxiv_id":"1803.08475","n_code_links":15,"syntology":{"ran":27,"of":40,"n_ran_checked":20,"n_instrument":7,"unverified":13,"pointer_only":5,"phrase":"27 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 3 honoured, 0 violated, 17 with no contract checked; 7 where Syntology's instrument failed) · 13 unverified","official":{"repos":["wouterkool/attention-tsp","wouterkool/attention-learn-to-route"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"dyan-a-dynamical-atoms-based-network-for","title":"DYAN: A Dynamical Atoms-Based Network for Video Prediction","date":"2018-03-20","arxiv_id":"1803.07201","n_code_links":0,"syntology":null},{"paper":"/paper/learning-dynamic-memory-networks-for-object","slug":"learning-dynamic-memory-networks-for-object","title":"Learning Dynamic Memory Networks for Object Tracking","date":"2018-03-20","arxiv_id":"1803.07268","n_code_links":1,"syntology":{"ran":1,"of":7,"n_ran_checked":1,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["skyoung/MemTrack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/removing-confounding-factors-associated","slug":"removing-confounding-factors-associated","title":"Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications","date":"2018-03-20","arxiv_id":"1803.07276","n_code_links":1,"syntology":null},{"paper":null,"slug":"stacked-neural-networks-for-end-to-end","title":"Stacked Neural Networks for end-to-end ciliary motion analysis","date":"2018-03-20","arxiv_id":"1803.07534","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-predictability-of-range-based","title":"Exploring the predictability of range-based volatility estimators using RNNs","date":"2018-03-19","arxiv_id":"1803.07152","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-modality-image-synthesis-from-unpaired","title":"Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size","date":"2018-03-18","arxiv_id":"1803.06629","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-neural-architecture-construction-using","title":"Fast Neural Architecture Construction using EnvelopeNets","date":"2018-03-18","arxiv_id":"1803.06744","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-economics-and-financial-time","title":"Forecasting Economics and Financial Time Series: ARIMA vs. LSTM","date":"2018-03-16","arxiv_id":"1803.06386","n_code_links":0,"syntology":null},{"paper":null,"slug":"c-lstm-enabling-efficient-lstm-using","title":"C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs","date":"2018-03-14","arxiv_id":"1803.06305","n_code_links":0,"syntology":null},{"paper":"/paper/independently-recurrent-neural-network-indrnn","slug":"independently-recurrent-neural-network-indrnn","title":"Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN","date":"2018-03-13","arxiv_id":"1803.04831","n_code_links":11,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Sunnydreamrain/IndRNN_Theano_Lasagne"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/lcanet-end-to-end-lipreading-with-cascaded","slug":"lcanet-end-to-end-lipreading-with-cascaded","title":"LCANet: End-to-End Lipreading with Cascaded Attention-CTC","date":"2018-03-13","arxiv_id":"1803.04988","n_code_links":0,"syntology":null},{"paper":null,"slug":"resource-aware-design-of-a-deep-convolutional","title":"Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion","date":"2018-03-13","arxiv_id":"1803.04840","n_code_links":0,"syntology":null},{"paper":null,"slug":"entity-aware-language-model-as-an","title":"Entity-Aware Language Model as an Unsupervised Reranker","date":"2018-03-12","arxiv_id":"1803.04291","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-nodes-to-networks-evolving-recurrent","title":"From Nodes to Networks: Evolving Recurrent Neural Networks","date":"2018-03-12","arxiv_id":"1803.04439","n_code_links":0,"syntology":null},{"paper":null,"slug":"armdn-associative-and-recurrent-mixture","title":"ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting","date":"2018-03-10","arxiv_id":"1803.03800","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-identification-of-bengali-english","title":"Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models","date":"2018-03-10","arxiv_id":"1803.03859","n_code_links":0,"syntology":null},{"paper":null,"slug":"community-interaction-and-conflict-on-the-web","title":"Community Interaction and Conflict on the Web","date":"2018-03-09","arxiv_id":"1803.03697","n_code_links":0,"syntology":null},{"paper":null,"slug":"arbitrary-discrete-sequence-anomaly-detection","title":"Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM","date":"2018-03-06","arxiv_id":"1803.02395","n_code_links":0,"syntology":null},{"paper":null,"slug":"cliner-20-accessible-and-accurate-clinical","title":"CliNER 2.0: Accessible and Accurate Clinical Concept Extraction","date":"2018-03-06","arxiv_id":"1803.02245","n_code_links":0,"syntology":null},{"paper":null,"slug":"caesar-context-awareness-enabled-summary","title":"CAESAR: Context Awareness Enabled Summary-Attentive Reader","date":"2018-03-04","arxiv_id":"1803.01335","n_code_links":0,"syntology":null},{"paper":"/paper/seq2sick-evaluating-the-robustness-of","slug":"seq2sick-evaluating-the-robustness-of","title":"Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples","date":"2018-03-03","arxiv_id":"1803.01128","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["cmhcbb/Seq2Sick"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"paper":null,"slug":"learning-sparse-structured-ensembles-with-sg","title":"Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning","date":"2018-03-01","arxiv_id":"1803.00184","n_code_links":0,"syntology":null},{"paper":"/paper/temporally-identity-aware-ssd-with","slug":"temporally-identity-aware-ssd-with","title":"Temporally Identity-Aware SSD with Attentional LSTM","date":"2018-03-01","arxiv_id":"1803.00197","n_code_links":1,"syntology":null},{"paper":"/paper/joint-pixel-and-feature-level-domain","slug":"joint-pixel-and-feature-level-domain","title":"Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild","date":"2018-02-28","arxiv_id":"1803.00068","n_code_links":1,"syntology":null},{"paper":"/paper/augmented-cyclegan-learning-many-to-many","slug":"augmented-cyclegan-learning-many-to-many","title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","date":"2018-02-27","arxiv_id":"1802.10151","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/improving-ocr-accuracy-on-early-printed-books-1","slug":"improving-ocr-accuracy-on-early-printed-books-1","title":"Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks","date":"2018-02-27","arxiv_id":"1802.10033","n_code_links":1,"syntology":null},{"paper":"/paper/on-extended-long-short-term-memory-and","slug":"on-extended-long-short-term-memory-and","title":"On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network","date":"2018-02-27","arxiv_id":"1803.01686","n_code_links":1,"syntology":null},{"paper":null,"slug":"rehar-robust-and-efficient-human-activity","title":"ReHAR: Robust and Efficient Human Activity Recognition","date":"2018-02-27","arxiv_id":"1802.09745","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-feed-forward-sequential-memory-networks","title":"Deep Feed-forward Sequential Memory Networks for Speech Synthesis","date":"2018-02-26","arxiv_id":"1802.09194","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-neural-networks-understand-logical","title":"Can Neural Networks Understand Logical Entailment?","date":"2018-02-23","arxiv_id":"1802.08535","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-neural-audio-synthesis","slug":"efficient-neural-audio-synthesis","title":"Efficient Neural Audio Synthesis","date":"2018-02-23","arxiv_id":"1802.08435","n_code_links":16,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/interpretable-charge-predictions-for-criminal","slug":"interpretable-charge-predictions-for-criminal","title":"Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions","date":"2018-02-23","arxiv_id":"1802.08504","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-algorithm-for-data-driven","title":"Deep learning algorithm for data-driven simulation of noisy dynamical system","date":"2018-02-22","arxiv_id":"1802.08323","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-order-recurrent-neural-networks-for","title":"High Order Recurrent Neural Networks for Acoustic Modelling","date":"2018-02-22","arxiv_id":"1802.08314","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-person-re-identification-by-temporal","title":"Video Person Re-identification by Temporal Residual Learning","date":"2018-02-22","arxiv_id":"1802.07918","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-forecasting-of-high-dimensional","title":"Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks","date":"2018-02-21","arxiv_id":"1802.07486","n_code_links":0,"syntology":null},{"paper":null,"slug":"generic-coreset-for-scalable-learning-of","title":"Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more","date":"2018-02-21","arxiv_id":"1802.07382","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-generative-modeling-approach-to-limited","title":"A Generative Modeling Approach to Limited Channel ECG Classification","date":"2018-02-18","arxiv_id":"1802.06458","n_code_links":0,"syntology":null},{"paper":null,"slug":"sequence-to-sequence-prediction-of-vehicle","title":"Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture","date":"2018-02-18","arxiv_id":"1802.06338","n_code_links":0,"syntology":null},{"paper":"/paper/a-machine-learning-approach-for-virtual-flow","slug":"a-machine-learning-approach-for-virtual-flow","title":"A Machine Learning Approach for Virtual Flow Metering and Forecasting","date":"2018-02-15","arxiv_id":"1802.05698","n_code_links":1,"syntology":null},{"paper":"/paper/cnnlstm-architecture-for-speech-emotion","slug":"cnnlstm-architecture-for-speech-emotion","title":"CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation","date":"2018-02-15","arxiv_id":"1802.05630","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-topic-argument-mining-from","title":"Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks","date":"2018-02-15","arxiv_id":"1802.05758","n_code_links":0,"syntology":null},{"paper":"/paper/deep-contextualized-word-representations","slug":"deep-contextualized-word-representations","title":"Deep contextualized word representations","date":"2018-02-15","arxiv_id":"1802.05365","n_code_links":46,"syntology":{"ran":30,"of":58,"n_ran_checked":24,"n_instrument":6,"unverified":28,"pointer_only":25,"phrase":"30 ran (of which 12 constructed an object rather than computing a result; 24 with no instrument failure: 2 honoured, 0 violated, 22 with no contract checked; 6 where Syntology's instrument failed) · 28 unverified","official":null}},{"paper":null,"slug":"deep-learning-for-lip-reading-using-audio","title":"Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language","date":"2018-02-15","arxiv_id":"1802.05521","n_code_links":0,"syntology":null},{"paper":"/paper/dr-bilstm-dependent-reading-bidirectional","slug":"dr-bilstm-dependent-reading-bidirectional","title":"DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference","date":"2018-02-15","arxiv_id":"1802.05577","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-and-data-assimilation-for-real","title":"Deep Learning and Data Assimilation for Real-Time Production Prediction in Natural Gas Wells","date":"2018-02-14","arxiv_id":"1802.05141","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-sentence-extraction-from","title":"Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data","date":"2018-02-13","arxiv_id":"1802.04675","n_code_links":0,"syntology":null},{"paper":null,"slug":"identify-susceptible-locations-in-medical","title":"Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models","date":"2018-02-13","arxiv_id":"1802.04822","n_code_links":0,"syntology":null},{"paper":"/paper/deep-neural-networks-for-bot-detection","slug":"deep-neural-networks-for-bot-detection","title":"Deep Neural Networks for Bot Detection","date":"2018-02-12","arxiv_id":"1802.04289","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"an-lstm-recurrent-network-for-step-counting","title":"An LSTM Recurrent Network for Step Counting","date":"2018-02-10","arxiv_id":"1802.03486","n_code_links":0,"syntology":null},{"paper":null,"slug":"invertible-autoencoder-for-domain-adaptation","title":"Invertible Autoencoder for domain adaptation","date":"2018-02-10","arxiv_id":"1802.06869","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-neural-architecture-search-via-1","slug":"efficient-neural-architecture-search-via-1","title":"Efficient Neural Architecture Search via Parameter Sharing","date":"2018-02-09","arxiv_id":"1802.03268","n_code_links":28,"syntology":{"ran":18,"of":38,"n_ran_checked":12,"n_instrument":6,"unverified":20,"pointer_only":25,"phrase":"18 ran (of which 10 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 6 where Syntology's instrument failed) · 20 unverified","official":null}},{"paper":null,"slug":"multiple-target-tracking-by-learning-feature","title":"Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly","date":"2018-02-09","arxiv_id":"1802.03252","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-neural-network-based-semantic","slug":"recurrent-neural-network-based-semantic","title":"Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning","date":"2018-02-09","arxiv_id":"1802.03238","n_code_links":1,"syntology":null},{"paper":"/paper/learning-to-score-the-figure-skating-sports","slug":"learning-to-score-the-figure-skating-sports","title":"Learning to score the figure skating sports videos","date":"2018-02-08","arxiv_id":"1802.02774","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepheart-semi-supervised-sequence-learning","title":"DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction","date":"2018-02-07","arxiv_id":"1802.02511","n_code_links":0,"syntology":null},{"paper":null,"slug":"effective-quantization-approaches-for","title":"Effective Quantization Approaches for Recurrent Neural Networks","date":"2018-02-07","arxiv_id":"1802.02615","n_code_links":0,"syntology":null},{"paper":"/paper/enhance-word-representation-for-out-of","slug":"enhance-word-representation-for-out-of","title":"Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus","date":"2018-02-07","arxiv_id":"1802.02614","n_code_links":1,"syntology":null},{"paper":"/paper/impala-scalable-distributed-deep-rl-with","slug":"impala-scalable-distributed-deep-rl-with","title":"IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures","date":"2018-02-05","arxiv_id":"1802.01561","n_code_links":24,"syntology":{"ran":16,"of":34,"n_ran_checked":10,"n_instrument":6,"unverified":18,"pointer_only":3,"phrase":"16 ran (of which 6 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 6 where Syntology's instrument failed) · 18 unverified","official":{"repos":["deepmind/scalable_agent"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/memory-fusion-network-for-multi-view","slug":"memory-fusion-network-for-multi-view","title":"Memory Fusion Network for Multi-view Sequential Learning","date":"2018-02-03","arxiv_id":"1802.00927","n_code_links":2,"syntology":null},{"paper":"/paper/multimodal-sentiment-analysis-with-word-level","slug":"multimodal-sentiment-analysis-with-word-level","title":"Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning","date":"2018-02-03","arxiv_id":"1802.00924","n_code_links":2,"syntology":null},{"paper":null,"slug":"deep-temporal-lstm-for-daily-living-action","title":"Deep-Temporal LSTM for Daily Living Action Recognition","date":"2018-02-01","arxiv_id":"1802.00421","n_code_links":0,"syntology":null},{"paper":"/paper/nested-lstms","slug":"nested-lstms","title":"Nested LSTMs","date":"2018-01-31","arxiv_id":"1801.10308","n_code_links":1,"syntology":null},{"paper":null,"slug":"one-class-collective-anomaly-detection-based","title":"One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks","date":"2018-01-31","arxiv_id":"1802.00324","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploration-on-generating-traditional-chinese","title":"Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method","date":"2018-01-27","arxiv_id":"1801.09030","n_code_links":0,"syntology":null},{"paper":"/paper/deepwriting-making-digital-ink-editable-via","slug":"deepwriting-making-digital-ink-editable-via","title":"DeepWriting: Making Digital Ink Editable via Deep Generative Modeling","date":"2018-01-25","arxiv_id":"1801.08379","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["emreaksan/deepwriting"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hybrid-gradient-boosting-trees-and-neural","title":"Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data","date":"2018-01-23","arxiv_id":"1801.07384","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-video-saliency-a-large-scale","slug":"revisiting-video-saliency-a-large-scale","title":"Revisiting Video Saliency: A Large-scale Benchmark and a New Model","date":"2018-01-23","arxiv_id":"1801.07424","n_code_links":1,"syntology":null},{"paper":"/paper/e-swish-adjusting-activations-to-different","slug":"e-swish-adjusting-activations-to-different","title":"E-swish: Adjusting Activations to Different Network Depths","date":"2018-01-22","arxiv_id":"1801.07145","n_code_links":1,"syntology":null},{"paper":null,"slug":"handwriting-trajectory-recovery-using-end-to","title":"Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network","date":"2018-01-22","arxiv_id":"1801.07211","n_code_links":0,"syntology":null},{"paper":"/paper/universal-language-model-fine-tuning-for-text","slug":"universal-language-model-fine-tuning-for-text","title":"Universal Language Model Fine-tuning for Text Classification","date":"2018-01-18","arxiv_id":"1801.06146","n_code_links":66,"syntology":{"ran":2,"of":5,"n_ran_checked":0,"n_instrument":2,"unverified":3,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["fastai/fastai"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/beyond-word-importance-contextual","slug":"beyond-word-importance-contextual","title":"Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs","date":"2018-01-16","arxiv_id":"1801.05453","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jamie-murdoch/ContextualDecomposition"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/unsupervised-cipher-cracking-using-discrete","slug":"unsupervised-cipher-cracking-using-discrete","title":"Unsupervised Cipher Cracking Using Discrete GANs","date":"2018-01-15","arxiv_id":"1801.04883","n_code_links":1,"syntology":null},{"paper":"/paper/multivariate-lstm-fcns-for-time-series","slug":"multivariate-lstm-fcns-for-time-series","title":"Multivariate LSTM-FCNs for Time Series Classification","date":"2018-01-14","arxiv_id":"1801.04503","n_code_links":7,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["houshd/MLSTM-FCN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"approximate-fpga-based-lstms-under","title":"Approximate FPGA-based LSTMs under Computation Time Constraints","date":"2018-01-07","arxiv_id":"1801.02190","n_code_links":0,"syntology":null},{"paper":"/paper/deep-bidirectional-and-unidirectional-lstm","slug":"deep-bidirectional-and-unidirectional-lstm","title":"Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction","date":"2018-01-07","arxiv_id":"1801.02143","n_code_links":1,"syntology":null},{"paper":null,"slug":"gated-recurrent-networks-for-seizure","title":"Gated Recurrent Networks for Seizure Detection","date":"2018-01-03","arxiv_id":"1801.02471","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-architectures-data-and-units-for","title":"Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer","date":"2018-01-02","arxiv_id":"1801.00841","n_code_links":0,"syntology":null},{"paper":"/paper/a-compressed-sensing-view-of-unsupervised","slug":"a-compressed-sensing-view-of-unsupervised","title":"A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs","date":"2018-01-01","arxiv_id":null,"n_code_links":2,"syntology":null}],"record_sha256":"b49de6d4c99ff2acda2a6e4121c6f30e8f8a68671df1c6fa7462c397a8e80165","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}