{"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/classification/papers/38","list_of":"/task/classification","task":"General Classification","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":38,"pages_in_order":146,"rows_per_page":100,"rows":[3701,3800],"of":14581,"counts":{"archive_papers_tagged":14581,"with_a_code_link":3945,"where_syntology_ran_a_sample":713,"not_listed_spam_title":0,"listed":14581,"listed_where_code_ran":713,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":560,"every_run_a_failure_of_syntologys_instrument":153,"listed_with_a_run_with_no_instrument_failure":560,"listed_every_run_a_failure_of_syntologys_instrument":153,"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/classification","prev":"/task/classification/papers/37","next":"/task/classification/papers/39","papers":[{"url":"/paper/temporal-attention-gated-model-for-robust","slug":"temporal-attention-gated-model-for-robust","title":"Temporal Attention-Gated Model for Robust Sequence Classification","date":"2016-12-01","arxiv_id":"1612.00385","repositories_listed":1,"syntology":null},{"url":"/paper/weighted-neural-bag-of-n-grams-model-new","slug":"weighted-neural-bag-of-n-grams-model-new","title":"Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-embeddings-and-convolutional-neural","slug":"word-embeddings-and-convolutional-neural","title":"Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sla-violation-prediction-in-cloud-computing-a","slug":"sla-violation-prediction-in-cloud-computing-a","title":"SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective","date":"2016-11-30","arxiv_id":"1611.10338","repositories_listed":1,"syntology":null},{"url":"/paper/learning-deep-representations-using","slug":"learning-deep-representations-using","title":"Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections","date":"2016-11-28","arxiv_id":"1611.09119","repositories_listed":1,"syntology":null},{"url":"/paper/bidirectional-tree-structured-lstm-with-head","slug":"bidirectional-tree-structured-lstm-with-head","title":"Bidirectional Tree-Structured LSTM with Head Lexicalization","date":"2016-11-21","arxiv_id":"1611.06788","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-for-lexicon-based","slug":"unsupervised-learning-for-lexicon-based","title":"Unsupervised Learning for Lexicon-Based Classification","date":"2016-11-21","arxiv_id":"1611.06933","repositories_listed":1,"syntology":null},{"url":"/paper/delugenets-deep-networks-with-efficient-and","slug":"delugenets-deep-networks-with-efficient-and","title":"DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows","date":"2016-11-17","arxiv_id":"1611.05552","repositories_listed":1,"syntology":null},{"url":"/paper/algebraic-multigrid-support-vector-machines","slug":"algebraic-multigrid-support-vector-machines","title":"Algebraic multigrid support vector machines","date":"2016-11-16","arxiv_id":"1611.05487","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transfer-learning-for-person-re","slug":"deep-transfer-learning-for-person-re","title":"Deep Transfer Learning for Person Re-identification","date":"2016-11-16","arxiv_id":"1611.05244","repositories_listed":1,"syntology":null},{"url":"/paper/fully-adaptive-feature-sharing-in-multi-task","slug":"fully-adaptive-feature-sharing-in-multi-task","title":"Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification","date":"2016-11-16","arxiv_id":"1611.05377","repositories_listed":1,"syntology":null},{"url":"/paper/octnet-learning-deep-3d-representations-at","slug":"octnet-learning-deep-3d-representations-at","title":"OctNet: Learning Deep 3D Representations at High Resolutions","date":"2016-11-15","arxiv_id":"1611.05009","repositories_listed":1,"syntology":null},{"url":"/paper/character-level-convolutional-network-for","slug":"character-level-convolutional-network-for","title":"Character-level Convolutional Network for Text Classification Applied to Chinese Corpus","date":"2016-11-14","arxiv_id":"1611.04358","repositories_listed":1,"syntology":null},{"url":"/paper/realistic-risk-mitigating-recommendations-via","slug":"realistic-risk-mitigating-recommendations-via","title":"Realistic risk-mitigating recommendations via inverse classification","date":"2016-11-13","arxiv_id":"1611.04199","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-navigate-in-complex-environments","slug":"learning-to-navigate-in-complex-environments","title":"Learning to Navigate in Complex Environments","date":"2016-11-11","arxiv_id":"1611.03673","repositories_listed":1,"syntology":null},{"url":"/paper/incremental-sequence-learning","slug":"incremental-sequence-learning","title":"Incremental Sequence Learning","date":"2016-11-09","arxiv_id":"1611.03068","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/incremental-sequence-learning#ran","syntology_url":"https://syntology.ai/paper/1611.03068","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.03068"}},"official":{"repos":["edwin-de-jong/incremental-sequence-learning"],"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"]}}},{"url":"/paper/ac-blstm-asymmetric-convolutional","slug":"ac-blstm-asymmetric-convolutional","title":"AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification","date":"2016-11-07","arxiv_id":"1611.01884","repositories_listed":1,"syntology":null},{"url":"/paper/deepsense-a-unified-deep-learning-framework","slug":"deepsense-a-unified-deep-learning-framework","title":"DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing","date":"2016-11-07","arxiv_id":"1611.01942","repositories_listed":1,"syntology":null},{"url":"/paper/log-time-and-log-space-extreme-classification","slug":"log-time-and-log-space-extreme-classification","title":"Log-time and Log-space Extreme Classification","date":"2016-11-07","arxiv_id":"1611.01964","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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) · 4 unverified","sample_list":"/paper/log-time-and-log-space-extreme-classification#ran","syntology_url":"https://syntology.ai/paper/1611.01964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.01964"}},"official":null}},{"url":"/paper/one-class-splitting-criteria-for-random","slug":"one-class-splitting-criteria-for-random","title":"One Class Splitting Criteria for Random Forests","date":"2016-11-07","arxiv_id":"1611.01971","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-deep-learning-by-metric","slug":"semi-supervised-deep-learning-by-metric","title":"Semi-supervised deep learning by metric embedding","date":"2016-11-04","arxiv_id":"1611.01449","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-network-language-models","slug":"convolutional-neural-network-language-models","title":"Convolutional Neural Network Language Models","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-sentiment-classification-with-user-and","slug":"neural-sentiment-classification-with-user-and","title":"Neural Sentiment Classification with User and Product Attention","date":"2016-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fuzzy-bayesian-learning","slug":"fuzzy-bayesian-learning","title":"Fuzzy Bayesian Learning","date":"2016-10-28","arxiv_id":"1610.09156","repositories_listed":1,"syntology":null},{"url":"/paper/missing-data-imputation-for-supervised","slug":"missing-data-imputation-for-supervised","title":"Missing Data Imputation for Supervised Learning","date":"2016-10-28","arxiv_id":"1610.09075","repositories_listed":1,"syntology":null},{"url":"/paper/sol-a-library-for-scalable-online-learning","slug":"sol-a-library-for-scalable-online-learning","title":"SOL: A Library for Scalable Online Learning Algorithms","date":"2016-10-28","arxiv_id":"1610.09083","repositories_listed":1,"syntology":null},{"url":"/paper/word-embeddings-for-the-construction-domain","slug":"word-embeddings-for-the-construction-domain","title":"Word Embeddings for the Construction Domain","date":"2016-10-28","arxiv_id":"1610.09333","repositories_listed":1,"syntology":null},{"url":"/paper/cogalex-v-shared-task-lexnet-integrated-path","slug":"cogalex-v-shared-task-lexnet-integrated-path","title":"CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations","date":"2016-10-27","arxiv_id":"1610.08694","repositories_listed":1,"syntology":null},{"url":"/paper/encoding-temporal-markov-dynamics-in-graph","slug":"encoding-temporal-markov-dynamics-in-graph","title":"Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series","date":"2016-10-24","arxiv_id":"1610.07273","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-with-deconvolution","slug":"representation-learning-with-deconvolution","title":"Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization","date":"2016-10-24","arxiv_id":"1610.07258","repositories_listed":1,"syntology":null},{"url":"/paper/cross-device-matching-for-online-advertising","slug":"cross-device-matching-for-online-advertising","title":"Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016","date":"2016-10-23","arxiv_id":"1610.07119","repositories_listed":1,"syntology":null},{"url":"/paper/m2cai-workflow-challenge-convolutional-neural","slug":"m2cai-workflow-challenge-convolutional-neural","title":"M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification","date":"2016-10-18","arxiv_id":"1610.05541","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-metric-learning-for-the-analysis-of","slug":"efficient-metric-learning-for-the-analysis-of","title":"Efficient Metric Learning for the Analysis of Motion Data","date":"2016-10-17","arxiv_id":"1610.05083","repositories_listed":1,"syntology":null},{"url":"/paper/minimax-filter-learning-to-preserve-privacy","slug":"minimax-filter-learning-to-preserve-privacy","title":"Minimax Filter: Learning to Preserve Privacy from Inference Attacks","date":"2016-10-12","arxiv_id":"1610.03577","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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) · 1 unverified","sample_list":"/paper/minimax-filter-learning-to-preserve-privacy#ran","syntology_url":"https://syntology.ai/paper/1610.03577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.03577"}},"official":{"repos":["jihunhamm/MinimaxFilter"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/variance-based-regularization-with-convex","slug":"variance-based-regularization-with-convex","title":"Variance-based regularization with convex objectives","date":"2016-10-08","arxiv_id":"1610.02581","repositories_listed":1,"syntology":null},{"url":"/paper/nonlinear-systems-identification-using-deep","slug":"nonlinear-systems-identification-using-deep","title":"Nonlinear Systems Identification Using Deep Dynamic Neural Networks","date":"2016-10-05","arxiv_id":"1610.01439","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-intermediate-layers-using","slug":"understanding-intermediate-layers-using","title":"Understanding intermediate layers using linear classifier probes","date":"2016-10-05","arxiv_id":"1610.01644","repositories_listed":1,"syntology":null},{"url":"/paper/seeing-into-darkness-scotopic-visual","slug":"seeing-into-darkness-scotopic-visual","title":"Seeing into Darkness: Scotopic Visual Recognition","date":"2016-10-03","arxiv_id":"1610.00405","repositories_listed":1,"syntology":null},{"url":"/paper/caffeinated-fpgas-fpga-framework-for","slug":"caffeinated-fpgas-fpga-framework-for","title":"Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks","date":"2016-09-30","arxiv_id":"1609.09671","repositories_listed":1,"syntology":null},{"url":"/paper/automated-breast-lesion-segmentation-in","slug":"automated-breast-lesion-segmentation-in","title":"Automated Breast Lesion Segmentation in Ultrasound Images","date":"2016-09-27","arxiv_id":"1609.08364","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-methods-for-prediction-with","slug":"multi-label-methods-for-prediction-with","title":"Multi-label Methods for Prediction with Sequential Data","date":"2016-09-27","arxiv_id":"1609.08349","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-neural-network-hyperparameters","slug":"optimizing-neural-network-hyperparameters","title":"Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification","date":"2016-09-27","arxiv_id":"1609.08703","repositories_listed":1,"syntology":null},{"url":"/paper/derivative-delay-embedding-online-modeling-of","slug":"derivative-delay-embedding-online-modeling-of","title":"Derivative Delay Embedding: Online Modeling of Streaming Time Series","date":"2016-09-24","arxiv_id":"1609.07540","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-video-classification-and","slug":"deep-learning-for-video-classification-and","title":"Deep Learning for Video Classification and Captioning","date":"2016-09-22","arxiv_id":"1609.06782","repositories_listed":1,"syntology":null},{"url":"/paper/image-embodied-knowledge-representation","slug":"image-embodied-knowledge-representation","title":"Image-embodied Knowledge Representation Learning","date":"2016-09-22","arxiv_id":"1609.07028","repositories_listed":1,"syntology":null},{"url":"/paper/how-should-we-evaluate-supervised-hashing","slug":"how-should-we-evaluate-supervised-hashing","title":"How should we evaluate supervised hashing?","date":"2016-09-21","arxiv_id":"1609.06753","repositories_listed":1,"syntology":null},{"url":"/paper/fastbdt-a-speed-optimized-and-cache-friendly","slug":"fastbdt-a-speed-optimized-and-cache-friendly","title":"FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification","date":"2016-09-20","arxiv_id":"1609.06119","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-adoption-of-abductive-reasoning-for","slug":"on-the-adoption-of-abductive-reasoning-for","title":"On the adoption of abductive reasoning for time series interpretation","date":"2016-09-19","arxiv_id":"1609.05632","repositories_listed":1,"syntology":null},{"url":"/paper/column-networks-for-collective-classification","slug":"column-networks-for-collective-classification","title":"Column Networks for Collective Classification","date":"2016-09-15","arxiv_id":"1609.04508","repositories_listed":1,"syntology":null},{"url":"/paper/a-greedy-algorithm-to-cluster-specialists","slug":"a-greedy-algorithm-to-cluster-specialists","title":"A Greedy Algorithm to Cluster Specialists","date":"2016-09-13","arxiv_id":"1609.03666","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-on-the-effects-of","slug":"an-empirical-study-on-the-effects-of","title":"An empirical study on the effects of different types of noise in image classification tasks","date":"2016-09-09","arxiv_id":"1609.02781","repositories_listed":1,"syntology":null},{"url":"/paper/fitted-learning-models-with-awareness-of","slug":"fitted-learning-models-with-awareness-of","title":"Fitted Learning: Models with Awareness of their Limits","date":"2016-09-07","arxiv_id":"1609.02226","repositories_listed":1,"syntology":null},{"url":"/paper/deep-retinal-image-understanding","slug":"deep-retinal-image-understanding","title":"Deep Retinal Image Understanding","date":"2016-09-05","arxiv_id":"1609.01103","repositories_listed":1,"syntology":null},{"url":"/paper/generic-inference-in-latent-gaussian-process","slug":"generic-inference-in-latent-gaussian-process","title":"Generic Inference in Latent Gaussian Process Models","date":"2016-09-02","arxiv_id":"1609.00577","repositories_listed":1,"syntology":null},{"url":"/paper/what-makes-imagenet-good-for-transfer","slug":"what-makes-imagenet-good-for-transfer","title":"What makes ImageNet good for transfer learning?","date":"2016-08-30","arxiv_id":"1608.08614","repositories_listed":1,"syntology":null},{"url":"/paper/business-process-deviance-mining-review-and","slug":"business-process-deviance-mining-review-and","title":"Business Process Deviance Mining: Review and Evaluation","date":"2016-08-29","arxiv_id":"1608.08252","repositories_listed":1,"syntology":null},{"url":"/paper/aid-a-benchmark-dataset-for-performance","slug":"aid-a-benchmark-dataset-for-performance","title":"AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification","date":"2016-08-18","arxiv_id":"1608.05167","repositories_listed":1,"syntology":null},{"url":"/paper/path-based-vs-distributional-information-in","slug":"path-based-vs-distributional-information-in","title":"Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations","date":"2016-08-17","arxiv_id":"1608.05014","repositories_listed":1,"syntology":null},{"url":"/paper/star-galaxy-classification-using-deep","slug":"star-galaxy-classification-using-deep","title":"Star-galaxy Classification Using Deep Convolutional Neural Networks","date":"2016-08-15","arxiv_id":"1608.04369","repositories_listed":1,"syntology":null},{"url":"/paper/deep-motif-dashboard-visualizing-and","slug":"deep-motif-dashboard-visualizing-and","title":"Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks","date":"2016-08-12","arxiv_id":"1608.03644","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-oriented-boundaries","slug":"convolutional-oriented-boundaries","title":"Convolutional Oriented Boundaries","date":"2016-08-09","arxiv_id":"1608.02755","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-the-city-quantifying-urban","slug":"deep-learning-the-city-quantifying-urban","title":"Deep Learning the City : Quantifying Urban Perception At A Global Scale","date":"2016-08-05","arxiv_id":"1608.01769","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-linear-dynamical-systems-from","slug":"analyzing-linear-dynamical-systems-from","title":"Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning","date":"2016-08-03","arxiv_id":"1608.01059","repositories_listed":1,"syntology":null},{"url":"/paper/cuhk-ethz-siat-submission-to-activitynet","slug":"cuhk-ethz-siat-submission-to-activitynet","title":"CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016","date":"2016-08-02","arxiv_id":"1608.00797","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-bidirectional-long-short-term","slug":"attention-based-bidirectional-long-short-term","title":"Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cntk-microsofts-open-source-deep-learning","slug":"cntk-microsofts-open-source-deep-learning","title":"CNTK: Microsoft's Open-Source Deep-Learning Toolkit","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/discourse-sense-classification-from-scratch","slug":"discourse-sense-classification-from-scratch","title":"Discourse Sense Classification from Scratch using Focused RNNs","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/qa-it-classifying-non-referential-it-for","slug":"qa-it-classifying-non-referential-it-for","title":"QA-It: Classifying Non-Referential It for Question Answer Pairs","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/identifying-and-harnessing-the-building","slug":"identifying-and-harnessing-the-building","title":"Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool","date":"2016-07-29","arxiv_id":"1607.08878","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-learning-from-multiple-noisy","slug":"interactive-learning-from-multiple-noisy","title":"Interactive Learning from Multiple Noisy Labels","date":"2016-07-24","arxiv_id":"1607.06988","repositories_listed":1,"syntology":null},{"url":"/paper/cgmos-certainty-guided-minority-oversampling","slug":"cgmos-certainty-guided-minority-oversampling","title":"CGMOS: Certainty Guided Minority OverSampling","date":"2016-07-21","arxiv_id":"1607.06525","repositories_listed":1,"syntology":null},{"url":"/paper/geometric-mean-metric-learning","slug":"geometric-mean-metric-learning","title":"Geometric Mean Metric Learning","date":"2016-07-18","arxiv_id":"1607.05002","repositories_listed":1,"syntology":null},{"url":"/paper/san-francisco-crime-classification","slug":"san-francisco-crime-classification","title":"San Francisco Crime Classification","date":"2016-07-13","arxiv_id":"1607.03626","repositories_listed":1,"syntology":null},{"url":"/paper/classifying-variable-length-audio-files-with","slug":"classifying-variable-length-audio-files-with","title":"Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling","date":"2016-07-11","arxiv_id":"1607.02857","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-metric-for-class-conditional-knn","slug":"learning-a-metric-for-class-conditional-knn","title":"Learning a metric for class-conditional KNN","date":"2016-07-11","arxiv_id":"1607.03050","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-quantile-additive-regression-trees","slug":"bayesian-quantile-additive-regression-trees","title":"Bayesian quantile additive regression trees","date":"2016-07-10","arxiv_id":"1607.02676","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-deep-convolutional-neural-networks","slug":"explaining-deep-convolutional-neural-networks","title":"Explaining Deep Convolutional Neural Networks on Music Classification","date":"2016-07-08","arxiv_id":"1607.02444","repositories_listed":1,"syntology":null},{"url":"/paper/untrimmed-video-classification-for-activity","slug":"untrimmed-video-classification-for-activity","title":"Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge","date":"2016-07-07","arxiv_id":"1607.01979","repositories_listed":1,"syntology":null},{"url":"/paper/videolstm-convolves-attends-and-flows-for","slug":"videolstm-convolves-attends-and-flows-for","title":"VideoLSTM Convolves, Attends and Flows for Action Recognition","date":"2016-07-06","arxiv_id":"1607.01794","repositories_listed":1,"syntology":null},{"url":"/paper/alzheimers-disease-diagnostics-by-a-deeply","slug":"alzheimers-disease-diagnostics-by-a-deeply","title":"Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network","date":"2016-07-02","arxiv_id":"1607.00556","repositories_listed":1,"syntology":null},{"url":"/paper/alzheimers-disease-diagnostics-by-adaptation","slug":"alzheimers-disease-diagnostics-by-adaptation","title":"Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network","date":"2016-07-02","arxiv_id":"1607.00455","repositories_listed":1,"syntology":null},{"url":"/paper/learning-crosslingual-word-embeddings-without","slug":"learning-crosslingual-word-embeddings-without","title":"Learning Crosslingual Word Embeddings without Bilingual Corpora","date":"2016-06-30","arxiv_id":"1606.09403","repositories_listed":1,"syntology":null},{"url":"/paper/discriminating-sample-groups-with-multi-way","slug":"discriminating-sample-groups-with-multi-way","title":"Discriminating sample groups with multi-way data","date":"2016-06-26","arxiv_id":"1606.08046","repositories_listed":1,"syntology":null},{"url":"/paper/deep-recurrent-neural-networks-for-supernovae","slug":"deep-recurrent-neural-networks-for-supernovae","title":"Deep Recurrent Neural Networks for Supernovae Classification","date":"2016-06-23","arxiv_id":"1606.07442","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-recurrent-neural-networks-for-supernovae#ran","syntology_url":"https://syntology.ai/paper/1606.07442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.07442"}},"official":{"repos":["adammoss/supernovae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-predictions-of-non-linear","slug":"explaining-predictions-of-non-linear","title":"Explaining Predictions of Non-Linear Classifiers in NLP","date":"2016-06-23","arxiv_id":"1606.07298","repositories_listed":1,"syntology":null},{"url":"/paper/non-convex-regularization-in-remote-sensing","slug":"non-convex-regularization-in-remote-sensing","title":"Non-convex regularization in remote sensing","date":"2016-06-23","arxiv_id":"1606.07289","repositories_listed":1,"syntology":null},{"url":"/paper/a-scalable-end-to-end-gaussian-process","slug":"a-scalable-end-to-end-gaussian-process","title":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","date":"2016-06-14","arxiv_id":"1606.04443","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_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) · 0 unverified","sample_list":"/paper/a-scalable-end-to-end-gaussian-process#ran","syntology_url":"https://syntology.ai/paper/1606.04443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.04443"}},"official":null}},{"url":"/paper/active-discriminative-text-representation","slug":"active-discriminative-text-representation","title":"Active Discriminative Text Representation Learning","date":"2016-06-14","arxiv_id":"1606.04212","repositories_listed":1,"syntology":null},{"url":"/paper/twise-at-semeval-2016-task-4-twitter","slug":"twise-at-semeval-2016-task-4-twitter","title":"TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification","date":"2016-06-14","arxiv_id":"1606.04351","repositories_listed":1,"syntology":null},{"url":"/paper/learning-semantically-and-additively","slug":"learning-semantically-and-additively","title":"Learning Semantically and Additively Compositional Distributional Representations","date":"2016-06-08","arxiv_id":"1606.02461","repositories_listed":1,"syntology":null},{"url":"/paper/neural-architectures-for-fine-grained-entity","slug":"neural-architectures-for-fine-grained-entity","title":"Neural Architectures for Fine-grained Entity Type Classification","date":"2016-06-04","arxiv_id":"1606.01341","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-job-title-classification-system","slug":"towards-a-job-title-classification-system","title":"Towards a Job Title Classification System","date":"2016-06-02","arxiv_id":"1606.00917","repositories_listed":1,"syntology":null},{"url":"/paper/unified-framework-for-quantification","slug":"unified-framework-for-quantification","title":"Unified Framework for Quantification","date":"2016-06-02","arxiv_id":"1606.00868","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-attention-networks-for-document","slug":"hierarchical-attention-networks-for-document","title":"Hierarchical Attention Networks for Document Classification","date":"2016-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-a-topic-model-for-sentences","slug":"on-a-topic-model-for-sentences","title":"On a Topic Model for Sentences","date":"2016-06-01","arxiv_id":"1606.00253","repositories_listed":1,"syntology":null},{"url":"/paper/pd-sparse-a-primal-and-dual-sparse-approach","slug":"pd-sparse-a-primal-and-dual-sparse-approach","title":"PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification","date":"2016-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/recognizing-emotions-from-abstract-paintings","slug":"recognizing-emotions-from-abstract-paintings","title":"Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion","date":"2016-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-budget-constrained-inverse-classification","slug":"a-budget-constrained-inverse-classification","title":"A budget-constrained inverse classification framework for smooth classifiers","date":"2016-05-29","arxiv_id":"1605.09068","repositories_listed":1,"syntology":null},{"url":"/paper/openxbow-introducing-the-passau-open-source","slug":"openxbow-introducing-the-passau-open-source","title":"openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit","date":"2016-05-22","arxiv_id":"1605.06778","repositories_listed":1,"syntology":null},{"url":"/paper/incremental-robot-learning-of-new-objects","slug":"incremental-robot-learning-of-new-objects","title":"Incremental Robot Learning of New Objects with Fixed Update Time","date":"2016-05-17","arxiv_id":"1605.05045","repositories_listed":1,"syntology":null}],"record_sha256":"cb7e0aa57b7067962362fcf10065c1c09203a0562e52461397e3fec0264c4a29","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}