{"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/svm/papers/15","list_of":"/method/svm","method":"SVM","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":15,"pages_in_order":19,"rows_per_page":100,"rows":[1401,1500],"of":1823,"counts":{"archive_papers_tagged":1823,"with_a_code_link":328,"where_syntology_ran_a_sample":28,"not_listed_spam_title":0,"listed":1823,"listed_where_code_ran":28,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/svm","prev":"/method/svm/papers/14","next":"/method/svm/papers/16","papers":[{"paper":null,"slug":"multi-word-entity-classification-in-a-highly","title":"Multi-word Entity Classification in a Highly Multilingual Environment","date":"2017-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ta14bingen-system-in-vardial-2017-shared-task","title":"T\\\"ubingen system in VarDial 2017 shared task: experiments with language identification and cross-lingual parsing","date":"2017-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-use-of-object-labels-and-spatial","title":"The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System","date":"2017-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"when-sparse-traditional-models-outperform","title":"When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages","date":"2017-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transductive-zero-shot-learning-with-adaptive","title":"Transductive Zero-Shot Learning with Adaptive Structural Embedding","date":"2017-03-27","arxiv_id":"1703.08897","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-stochastic-variance-reduced-gradient","title":"Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning","date":"2017-03-23","arxiv_id":"1703.07948","n_code_links":0,"syntology":null},{"paper":null,"slug":"legal-question-answering-using-ranking-svm","title":"Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network","date":"2017-03-16","arxiv_id":"1703.05320","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-margin-object-tracking-with-circulant","title":"Large Margin Object Tracking with Circulant Feature Maps","date":"2017-03-15","arxiv_id":"1703.05020","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-proximity-aware-hierarchical-clustering-of","title":"A Proximity-Aware Hierarchical Clustering of Faces","date":"2017-03-14","arxiv_id":"1703.04835","n_code_links":0,"syntology":null},{"paper":"/paper/recod-titans-at-isic-challenge-2017","slug":"recod-titans-at-isic-challenge-2017","title":"RECOD Titans at ISIC Challenge 2017","date":"2017-03-14","arxiv_id":"1703.04819","n_code_links":4,"syntology":null},{"paper":"/paper/deep-radial-kernel-networks-approximating","slug":"deep-radial-kernel-networks-approximating","title":"Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks","date":"2017-03-09","arxiv_id":"1703.03470","n_code_links":1,"syntology":null},{"paper":"/paper/fast-and-robust-detection-of-fallen-people","slug":"fast-and-robust-detection-of-fallen-people","title":"Fast and Robust Detection of Fallen People from a Mobile Robot","date":"2017-03-09","arxiv_id":"1703.03349","n_code_links":0,"syntology":null},{"paper":"/paper/easy-over-hard-a-case-study-on-deep-learning","slug":"easy-over-hard-a-case-study-on-deep-learning","title":"Easy over Hard: A Case Study on Deep Learning","date":"2017-03-01","arxiv_id":"1703.00133","n_code_links":1,"syntology":null},{"paper":null,"slug":"l3-svms-landmarks-based-linear-local-support","title":"L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines","date":"2017-03-01","arxiv_id":"1703.00284","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-extensive-technique-to-detect-and-analyze","title":"An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017","date":"2017-02-28","arxiv_id":"1702.08717","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-rates-for-classification-with","title":"Learning rates for classification with Gaussian kernels","date":"2017-02-28","arxiv_id":"1702.08701","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-of-instances-by-hashing","title":"Segmentation of Instances by Hashing","date":"2017-02-27","arxiv_id":"1702.08160","n_code_links":0,"syntology":null},{"paper":null,"slug":"support-vector-machine-and-its-bias","title":"Support vector machine and its bias correction in high-dimension, low-sample-size settings","date":"2017-02-26","arxiv_id":"1702.08019","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-social-network-multilingual","title":"Efficient Social Network Multilingual Classification using Character, POS n-grams and Dynamic Normalization","date":"2017-02-21","arxiv_id":"1702.06467","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-representation-learning-with-single","title":"Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification","date":"2017-02-21","arxiv_id":"1702.06456","n_code_links":0,"syntology":null},{"paper":null,"slug":"support-vector-machines-and-generalisation-in","title":"Support Vector Machines and generalisation in HEP","date":"2017-02-15","arxiv_id":"1702.04686","n_code_links":0,"syntology":null},{"paper":null,"slug":"mutual-kernel-matrix-completion","title":"Mutual Kernel Matrix Completion","date":"2017-02-14","arxiv_id":"1702.04077","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-decomposition-framework-for","title":"An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses","date":"2017-02-13","arxiv_id":"1702.03690","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-modified-construction-for-a-support-vector","title":"A Modified Construction for a Support Vector Classifier to Accommodate Class Imbalances","date":"2017-02-08","arxiv_id":"1702.02555","n_code_links":0,"syntology":null},{"paper":null,"slug":"backpropagation-training-for-fisher-vectors","title":"Backpropagation Training for Fisher Vectors within Neural Networks","date":"2017-02-08","arxiv_id":"1702.02549","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-feature-classifiers-for-burst-detection","title":"Multi-feature classifiers for burst detection in single EEG channels from preterm infants","date":"2017-02-08","arxiv_id":"1702.02873","n_code_links":0,"syntology":null},{"paper":null,"slug":"elixa-a-modular-and-flexible-absa-platform","title":"EliXa: A Modular and Flexible ABSA Platform","date":"2017-02-07","arxiv_id":"1702.01944","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-cost-sensitive-svm-for-imbalanced","title":"Optimizing Cost-Sensitive SVM for Imbalanced Data :Connecting Cluster to Classification","date":"2017-02-06","arxiv_id":"1702.01504","n_code_links":0,"syntology":null},{"paper":null,"slug":"exact-heat-kernel-on-a-hypersphere-and-its","title":"Exact heat kernel on a hypersphere and its applications in kernel SVM","date":"2017-02-05","arxiv_id":"1702.01373","n_code_links":0,"syntology":null},{"paper":null,"slug":"handwritten-recognition-using-svm-knn-and","title":"Handwritten Recognition Using SVM, KNN and Neural Network","date":"2017-02-01","arxiv_id":"1702.00723","n_code_links":0,"syntology":null},{"paper":null,"slug":"source-localization-in-an-ocean-waveguide","title":"Source localization in an ocean waveguide using supervised machine learning","date":"2017-01-29","arxiv_id":"1701.08431","n_code_links":0,"syntology":null},{"paper":"/paper/treelogy-a-novel-tree-classifier-utilizing","slug":"treelogy-a-novel-tree-classifier-utilizing","title":"Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations","date":"2017-01-28","arxiv_id":"1701.08291","n_code_links":1,"syntology":null},{"paper":null,"slug":"sign-language-recognition-using-temporal","title":"Sign Language Recognition Using Temporal Classification","date":"2017-01-07","arxiv_id":"1701.01875","n_code_links":0,"syntology":null},{"paper":null,"slug":"very-fast-kernel-svm-under-budget-constraints","title":"Very Fast Kernel SVM under Budget Constraints","date":"2016-12-31","arxiv_id":"1701.00167","n_code_links":0,"syntology":null},{"paper":"/paper/gensvm-a-generalized-multiclass-support","slug":"gensvm-a-generalized-multiclass-support","title":"GenSVM: A Generalized Multiclass Support Vector Machine","date":"2016-12-30","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"permuted-and-augmented-stick-breaking","title":"Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression","date":"2016-12-30","arxiv_id":"1612.09413","n_code_links":0,"syntology":null},{"paper":"/paper/what-is-relevant-in-a-text-document-an","slug":"what-is-relevant-in-a-text-document-an","title":"\"What is Relevant in a Text Document?\": An Interpretable Machine Learning Approach","date":"2016-12-23","arxiv_id":"1612.07843","n_code_links":1,"syntology":null},{"paper":null,"slug":"projected-semi-stochastic-gradient-descent","title":"Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption","date":"2016-12-16","arxiv_id":"1612.05356","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-image-action-recognition-using","title":"Single Image Action Recognition using Semantic Body Part Actions","date":"2016-12-14","arxiv_id":"1612.04520","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-and-optimization-of-loss-functions","slug":"analysis-and-optimization-of-loss-functions","title":"Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification","date":"2016-12-12","arxiv_id":"1612.03663","n_code_links":1,"syntology":null},{"paper":null,"slug":"tensor-dictionary-learning-with-deep-kruskal","title":"Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis","date":"2016-12-08","arxiv_id":"1612.02842","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalized-rbf-kernel-for-incomplete-data","title":"Generalized RBF kernel for incomplete data","date":"2016-12-05","arxiv_id":"1612.01480","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-localization-and-prediction-of-actions","title":"Online Localization and Prediction of Actions and Interactions","date":"2016-12-04","arxiv_id":"1612.01194","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-multiclasssvm-based-framework-to","title":"A novel multiclassSVM based framework to classify lithology from well logs: a real-world application","date":"2016-12-02","arxiv_id":"1612.00840","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-of-a-hybrid-learning-system-based","title":"Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS","date":"2016-12-02","arxiv_id":"1612.00585","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-deep-learning-architecture-for","title":"A Hybrid Deep Learning Architecture for Sentiment Analysis","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"asynchronous-parallel-greedy-coordinate","title":"Asynchronous Parallel Greedy Coordinate Descent","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cancer-hallmark-text-classification-using","title":"Cancer Hallmark Text Classification Using Convolutional Neural Networks","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"classifying-asr-transcriptions-according-to","title":"Classifying ASR Transcriptions According to Arabic Dialect","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"discriminating-between-similar-languages-and","title":"Discriminating between Similar Languages and Arabic Dialect Identification: A Report on the Third DSL Shared Task","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"discriminating-similar-languages-with-linear","title":"Discriminating Similar Languages with Linear SVMs and Neural Networks","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-decomposed-learning-with-factorwise","title":"Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"large-margin-discriminant-dimensionality","title":"Large Margin Discriminant Dimensionality Reduction in Prediction Space","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"qcri-dsl-2016-spoken-arabic-dialect","title":"QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"vanilla-classifiers-for-distinguishing","title":"Vanilla Classifiers for Distinguishing between Similar Languages","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"zara-a-virtual-interactive-dialogue-system","title":"Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"behavior-based-machine-learning-a-hybrid","title":"Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making","date":"2016-11-30","arxiv_id":"1611.10228","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-classification-of-connectomes-based-on","title":"Kernel classification of connectomes based on earth mover's distance between graph spectra","date":"2016-11-27","arxiv_id":"1611.08812","n_code_links":0,"syntology":null},{"paper":"/paper/distributed-optimization-of-multi-class-svms","slug":"distributed-optimization-of-multi-class-svms","title":"Distributed Optimization of Multi-Class SVMs","date":"2016-11-25","arxiv_id":"1611.08480","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-efficiency-of-svm-k-fold-cross","title":"Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding","date":"2016-11-23","arxiv_id":"1611.07659","n_code_links":0,"syntology":null},{"paper":null,"slug":"inferring-restaurant-styles-by-mining-crowd","title":"Inferring Restaurant Styles by Mining Crowd Sourced Photos from User-Review Websites","date":"2016-11-19","arxiv_id":"1611.06301","n_code_links":0,"syntology":null},{"paper":null,"slug":"backtracking-spatial-pyramid-pooling-spp","title":"Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection","date":"2016-11-16","arxiv_id":"1611.05345","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-learning-scheme-for-microgrid-islanding-and","title":"A Learning Scheme for Microgrid Islanding and Reconnection","date":"2016-11-15","arxiv_id":"1611.05317","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-semi-supervised-graph-classifier","title":"Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights","date":"2016-11-15","arxiv_id":"1611.04924","n_code_links":0,"syntology":null},{"paper":"/paper/trusting-svm-for-piecewise-linear-cnns","slug":"trusting-svm-for-piecewise-linear-cnns","title":"Trusting SVM for Piecewise Linear CNNs","date":"2016-11-07","arxiv_id":"1611.02185","n_code_links":2,"syntology":null},{"paper":null,"slug":"extracting-actionability-from-machine","title":"Extracting Actionability from Machine Learning Models by Sub-optimal Deterministic Planning","date":"2016-11-03","arxiv_id":"1611.00873","n_code_links":0,"syntology":null},{"paper":null,"slug":"support-vector-machines-and-generalisation-in-1","title":"Support Vector Machines and Generalisation in HEP","date":"2016-10-19","arxiv_id":"1610.09932","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-active-learning-for-support","title":"Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data","date":"2016-10-13","arxiv_id":"1610.03995","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-methodology-for-customizing-clinical-tests","title":"A Methodology for Customizing Clinical Tests for Esophageal Cancer based on Patient Preferences","date":"2016-10-06","arxiv_id":"1610.01712","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-regularizations-deep-learning-for","title":"Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8","date":"2016-10-06","arxiv_id":"1610.01795","n_code_links":0,"syntology":null},{"paper":null,"slug":"universum-learning-for-multiclass-svm","title":"Universum Learning for Multiclass SVM","date":"2016-09-29","arxiv_id":"1609.09162","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-nowcasting-method-based-on","title":"A Machine Learning Nowcasting Method based on Real-time Reanalysis Data","date":"2016-09-14","arxiv_id":"1609.04103","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-gender-classification-and-age","title":"Joint Gender Classification and Age Estimation by Nearly Orthogonalizing Their Semantic Spaces","date":"2016-09-14","arxiv_id":"1609.04116","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-and-classification-of-skin","title":"Segmentation and Classification of Skin Lesions for Disease Diagnosis","date":"2016-09-12","arxiv_id":"1609.03277","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-semantic-part-based-models-from","title":"Learning Semantic Part-Based Models from Google Images","date":"2016-09-11","arxiv_id":"1609.03140","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-spatial-pooler-of-hierarchical-temporal","title":"Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity","date":"2016-09-10","arxiv_id":"1609.03093","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-natural-language-processing-to-screen","title":"Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance","date":"2016-09-06","arxiv_id":"1609.01580","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-detection-for-short-text-messages-in","title":"Language Detection For Short Text Messages In Social Media","date":"2016-08-30","arxiv_id":"1608.08515","n_code_links":0,"syntology":null},{"paper":"/paper/machine-learning-in-downlink-coordinated","slug":"machine-learning-in-downlink-coordinated","title":"Machine Learning in Downlink Coordinated Multipoint in Heterogeneous Networks","date":"2016-08-30","arxiv_id":"1608.08306","n_code_links":1,"syntology":null},{"paper":null,"slug":"computer-aided-colorectal-tumor","title":"Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using CNN Features","date":"2016-08-24","arxiv_id":"1608.06709","n_code_links":0,"syntology":null},{"paper":null,"slug":"unbiased-learning-to-rank-with-biased","title":"Unbiased Learning-to-Rank with Biased Feedback","date":"2016-08-16","arxiv_id":"1608.04468","n_code_links":0,"syntology":null},{"paper":null,"slug":"viewpoint-and-topic-modeling-of-current","title":"Viewpoint and Topic Modeling of Current Events","date":"2016-08-14","arxiv_id":"1608.04089","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-prediction-of-gene-regulatory","title":"Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms","date":"2016-08-11","arxiv_id":"1608.03530","n_code_links":0,"syntology":null},{"paper":null,"slug":"communication-efficient-parallel-block","title":"Communication-Efficient Parallel Block Minimization for Kernel Machines","date":"2016-08-05","arxiv_id":"1608.02010","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-class-slab-support-vector-machine","title":"One-Class Slab Support Vector Machine","date":"2016-08-02","arxiv_id":"1608.01026","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatio-temporal-co-occurrence","title":"Spatio-temporal Co-Occurrence Characterizations for Human Action Classification","date":"2016-08-02","arxiv_id":"1610.05174","n_code_links":0,"syntology":null},{"paper":"/paper/stock-trend-prediction-using-news-sentiment","slug":"stock-trend-prediction-using-news-sentiment","title":"Stock trend prediction using news sentiment analysis","date":"2016-07-07","arxiv_id":"1607.01958","n_code_links":2,"syntology":null},{"paper":null,"slug":"stochastic-quasi-newton-methods-for-nonconvex","title":"Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization","date":"2016-07-05","arxiv_id":"1607.01231","n_code_links":0,"syntology":null},{"paper":null,"slug":"error-resilient-machine-learning-in-near","title":"Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble","date":"2016-07-03","arxiv_id":"1607.07804","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-energy-and-precision","title":"Understanding the Energy and Precision Requirements for Online Learning","date":"2016-07-03","arxiv_id":"1607.00669","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-word-embeddings-in-twitter-election","title":"Using Word Embeddings in Twitter Election Classification","date":"2016-06-22","arxiv_id":"1606.07006","n_code_links":0,"syntology":null},{"paper":"/paper/slack-and-margin-rescaling-as-convex","slug":"slack-and-margin-rescaling-as-convex","title":"Slack and Margin Rescaling as Convex Extensions of Supermodular Functions","date":"2016-06-19","arxiv_id":"1606.05918","n_code_links":1,"syntology":null},{"paper":null,"slug":"combining-multiscale-features-for","title":"Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach","date":"2016-06-15","arxiv_id":"1606.04985","n_code_links":0,"syntology":null},{"paper":null,"slug":"max-margin-feature-selection","title":"Max-Margin Feature Selection","date":"2016-06-14","arxiv_id":"1606.04506","n_code_links":0,"syntology":null},{"paper":null,"slug":"specialized-support-vector-machines-for-open","title":"Specialized Support Vector Machines for Open-set Recognition","date":"2016-06-13","arxiv_id":"1606.03802","n_code_links":0,"syntology":null},{"paper":"/paper/a-minimax-approach-to-supervised-learning","slug":"a-minimax-approach-to-supervised-learning","title":"A Minimax Approach to Supervised Learning","date":"2016-06-07","arxiv_id":"1606.02206","n_code_links":1,"syntology":null},{"paper":null,"slug":"curie-a-method-for-protecting-svm-classifier","title":"Curie: A method for protecting SVM Classifier from Poisoning Attack","date":"2016-06-05","arxiv_id":"1606.01584","n_code_links":0,"syntology":null},{"paper":null,"slug":"shallow-networks-for-high-accuracy-road","title":"Shallow Networks for High-Accuracy Road Object-Detection","date":"2016-06-05","arxiv_id":"1606.01561","n_code_links":0,"syntology":null},{"paper":"/paper/adversarially-learned-inference","slug":"adversarially-learned-inference","title":"Adversarially Learned Inference","date":"2016-06-02","arxiv_id":"1606.00704","n_code_links":9,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["IshmaelBelghazi/ALI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"670003dfbbb0d60fb2a254edf69ada0178ba2117390a9e4642e2453bb4d60b4d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}