{"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/affine-coupling/papers/2","list_of":"/method/affine-coupling","method":"Affine Coupling","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":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":230,"counts":{"archive_papers_tagged":230,"with_a_code_link":86,"where_syntology_ran_a_sample":23,"not_listed_spam_title":0,"listed":230,"listed_where_code_ran":23,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":18,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":18,"listed_every_run_a_failure_of_syntologys_instrument":5,"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/affine-coupling","prev":"/method/affine-coupling","next":"/method/affine-coupling/papers/3","papers":[{"paper":null,"slug":"seismoglow-data-augmentation-for-the-class","title":"SeismoFlow -- Data augmentation for the class imbalance problem","date":"2020-07-23","arxiv_id":"2007.12229","n_code_links":0,"syntology":null},{"paper":"/paper/discrete-point-flow-networks-for-efficient","slug":"discrete-point-flow-networks-for-efficient","title":"Discrete Point Flow Networks for Efficient Point Cloud Generation","date":"2020-07-20","arxiv_id":"2007.10170","n_code_links":1,"syntology":null},{"paper":"/paper/generative-flows-with-matrix-exponential","slug":"generative-flows-with-matrix-exponential","title":"Generative Flows with Matrix Exponential","date":"2020-07-19","arxiv_id":"2007.09651","n_code_links":1,"syntology":null},{"paper":"/paper/bison-bm25-weighted-self-attention-framework","slug":"bison-bm25-weighted-self-attention-framework","title":"GLOW : Global Weighted Self-Attention Network for Web Search","date":"2020-07-10","arxiv_id":"2007.05186","n_code_links":1,"syntology":null},{"paper":null,"slug":"coupling-based-invertible-neural-networks-are","title":"Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators","date":"2020-06-20","arxiv_id":"2006.11469","n_code_links":0,"syntology":null},{"paper":"/paper/moflow-an-invertible-flow-model-for","slug":"moflow-an-invertible-flow-model-for","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","date":"2020-06-17","arxiv_id":"2006.10137","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["calvin-zcx/moflow"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-comparison-of-vietnamese-statistical","title":"A comparison of Vietnamese Statistical Parametric Speech Synthesis Systems","date":"2020-05-26","arxiv_id":"2005.12962","n_code_links":0,"syntology":null},{"paper":"/paper/glow-tts-a-generative-flow-for-text-to-speech","slug":"glow-tts-a-generative-flow-for-text-to-speech","title":"Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search","date":"2020-05-22","arxiv_id":"2005.11129","n_code_links":6,"syntology":{"ran":13,"of":14,"n_ran_checked":11,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jaywalnut310/glow-tts"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"variational-autoencoders-with-normalizing-1","title":"Variational Autoencoders with Normalizing Flow Decoders","date":"2020-04-12","arxiv_id":"2004.05617","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-contrary-to-duty-with-cp-nets","title":"Modeling Contrary-to-Duty with CP-nets","date":"2020-03-23","arxiv_id":"2003.10480","n_code_links":0,"syntology":null},{"paper":"/paper/gaussianization-flows","slug":"gaussianization-flows","title":"Gaussianization Flows","date":"2020-03-04","arxiv_id":"2003.01941","n_code_links":3,"syntology":{"ran":16,"of":21,"n_ran_checked":15,"n_instrument":1,"unverified":5,"pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["chenlin9/Gaussianization_Flows"],"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":["listed","official"]}}},{"paper":"/paper/implicit-functions-in-feature-space-for-3d","slug":"implicit-functions-in-feature-space-for-3d","title":"Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion","date":"2020-03-03","arxiv_id":"2003.01456","n_code_links":1,"syntology":null},{"paper":null,"slug":"emosaic-visualizing-affective-content-of-text","title":"Emosaic: Visualizing Affective Content of Text at Varying Granularity","date":"2020-02-24","arxiv_id":"2002.10096","n_code_links":0,"syntology":null},{"paper":"/paper/schoenberg-rao-distances-entropy-based-and","slug":"schoenberg-rao-distances-entropy-based-and","title":"Schoenberg-Rao distances: Entropy-based and geometry-aware statistical Hilbert distances","date":"2020-02-19","arxiv_id":"2002.08345","n_code_links":1,"syntology":null},{"paper":"/paper/multimodal-controller-for-generative-models","slug":"multimodal-controller-for-generative-models","title":"Multimodal Controller for Generative Models","date":"2020-02-07","arxiv_id":"2002.02572","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-probabilistic-logic-reasoning-with-1","slug":"efficient-probabilistic-logic-reasoning-with-1","title":"Efficient Probabilistic Logic Reasoning with Graph Neural Networks","date":"2020-01-29","arxiv_id":"2001.11850","n_code_links":1,"syntology":null},{"paper":null,"slug":"fair-transfer-of-multiple-style-attributes-in","title":"Fair Transfer of Multiple Style Attributes in Text","date":"2020-01-18","arxiv_id":"2001.06693","n_code_links":0,"syntology":null},{"paper":"/paper/disentanglement-by-nonlinear-ica-with-general","slug":"disentanglement-by-nonlinear-ica-with-general","title":"Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)","date":"2020-01-14","arxiv_id":"2001.04872","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/no-spurious-local-minima-in-deep-quadratic","slug":"no-spurious-local-minima-in-deep-quadratic","title":"Avoiding Spurious Local Minima in Deep Quadratic Networks","date":"2019-12-31","arxiv_id":"2001.00098","n_code_links":1,"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/probing-the-phonetic-and-phonological","slug":"probing-the-phonetic-and-phonological","title":"Probing the phonetic and phonological knowledge of tones in Mandarin TTS models","date":"2019-12-23","arxiv_id":"1912.10915","n_code_links":1,"syntology":null},{"paper":"/paper/waveflow-a-compact-flow-based-model-for-raw-1","slug":"waveflow-a-compact-flow-based-model-for-raw-1","title":"WaveFlow: A Compact Flow-based Model for Raw Audio","date":"2019-12-03","arxiv_id":"1912.01219","n_code_links":4,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["PaddlePaddle/Parakeet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"how-much-over-parameterization-is-sufficient","title":"How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?","date":"2019-11-27","arxiv_id":"1911.12360","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-robustness-of-flow-based","title":"Adversarial Robustness of Flow-Based Generative Models","date":"2019-11-20","arxiv_id":"1911.08654","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-configuration-space-decomposition-scheme","title":"A Configuration-Space Decomposition Scheme for Learning-based Collision Checking","date":"2019-11-17","arxiv_id":"1911.08581","n_code_links":0,"syntology":null},{"paper":"/paper/any-precision-deep-neural-networks","slug":"any-precision-deep-neural-networks","title":"Any-Precision Deep Neural Networks","date":"2019-11-17","arxiv_id":"1911.07346","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["SHI-Labs/Any-Precision-DNNs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"speaker-independence-of-neural-vocoders-and","title":"Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement","date":"2019-11-14","arxiv_id":"1911.06266","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-generative-models-strike-back-improving","title":"Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data","date":"2019-11-12","arxiv_id":"1911.04699","n_code_links":0,"syntology":null},{"paper":null,"slug":"incentive-aware-contextual-pricing-with-non","title":"Incentive-aware Contextual Pricing with Non-parametric Market Noise","date":"2019-11-08","arxiv_id":"1911.03508","n_code_links":0,"syntology":null},{"paper":null,"slug":"small-gan-speeding-up-gan-training-using-core","title":"Small-GAN: Speeding Up GAN Training Using Core-sets","date":"2019-10-29","arxiv_id":"1910.13540","n_code_links":0,"syntology":null},{"paper":null,"slug":"transferring-neural-speech-waveform","title":"Transferring neural speech waveform synthesizers to musical instrument sounds generation","date":"2019-10-27","arxiv_id":"1910.12381","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-unbiased-risk-estimator-for-learning-with","title":"An Unbiased Risk Estimator for Learning with Augmented Classes","date":"2019-10-21","arxiv_id":"1910.09388","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-decentralized-coordinated-online","title":"Semi-Decentralized Coordinated Online Learning for Continuous Games with Coupled Constraints via Augmented Lagrangian","date":"2019-10-21","arxiv_id":"1910.09276","n_code_links":0,"syntology":null},{"paper":null,"slug":"label-conditioned-next-frame-video-generation","title":"Label-Conditioned Next-Frame Video Generation with Neural Flows","date":"2019-10-16","arxiv_id":"1910.11106","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-exploration-in-linear-contextual","title":"Adaptive Exploration in Linear Contextual Bandit","date":"2019-10-15","arxiv_id":"1910.06996","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-linearization-on-quadratic-and-higher","title":"Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks","date":"2019-10-03","arxiv_id":"1910.01619","n_code_links":0,"syntology":null},{"paper":null,"slug":"tails-of-triangular-flows","title":"Tails of Lipschitz Triangular Flows","date":"2019-07-10","arxiv_id":"1907.04481","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpolated-spectral-ngram-language-models","title":"Interpolated Spectral NGram Language Models","date":"2019-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"individual-fairness-in-sponsored-search","title":"Multi-Category Fairness in Sponsored Search Auctions","date":"2019-06-20","arxiv_id":"1906.08732","n_code_links":0,"syntology":null},{"paper":"/paper/parametric-resynthesis-with-neural-vocoders","slug":"parametric-resynthesis-with-neural-vocoders","title":"Parametric Resynthesis with neural vocoders","date":"2019-06-16","arxiv_id":"1906.06762","n_code_links":1,"syntology":null},{"paper":null,"slug":"generalization-guarantees-for-neural-networks","title":"Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian","date":"2019-06-12","arxiv_id":"1906.05392","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-the-robustness-of-nearest-neighbor","slug":"evaluating-the-robustness-of-nearest-neighbor","title":"Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective","date":"2019-06-10","arxiv_id":"1906.03972","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"distributed-training-with-heterogeneous-data","title":"Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms","date":"2019-06-04","arxiv_id":"1906.01736","n_code_links":0,"syntology":null},{"paper":null,"slug":"cic-at-semeval-2019-task-5-simple-yet-very","title":"CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/structured-output-learning-with-conditional","slug":"structured-output-learning-with-conditional","title":"Structured Output Learning with Conditional Generative Flows","date":"2019-05-30","arxiv_id":"1905.13288","n_code_links":2,"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":{"repos":["yolu1055/conditional-glow"],"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/discrete-flows-invertible-generative-models","slug":"discrete-flows-invertible-generative-models","title":"Discrete Flows: Invertible Generative Models of Discrete Data","date":"2019-05-24","arxiv_id":"1905.10347","n_code_links":2,"syntology":null},{"paper":null,"slug":"generative-flow-via-invertible-nxn","title":"Fast Flow Reconstruction via Robust Invertible nxn Convolution","date":"2019-05-24","arxiv_id":"1905.10170","n_code_links":0,"syntology":null},{"paper":"/paper/x2ct-gan-reconstructing-ct-from-biplanar-x","slug":"x2ct-gan-reconstructing-ct-from-biplanar-x","title":"X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks","date":"2019-05-16","arxiv_id":"1905.06902","n_code_links":1,"syntology":null},{"paper":null,"slug":"doublesqueeze-parallel-stochastic-gradient","title":"DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression","date":"2019-05-15","arxiv_id":"1905.05957","n_code_links":0,"syntology":null},{"paper":"/paper/deep-ordinal-reinforcement-learning","slug":"deep-ordinal-reinforcement-learning","title":"Deep Ordinal Reinforcement Learning","date":"2019-05-06","arxiv_id":"1905.02005","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-sampling-with-probability-matching","title":"$A^*$ sampling with probability matching","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"padam-closing-the-generalization-gap-of","title":"Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-skeleton-bridged-deep-learning-approach-for-2","slug":"a-skeleton-bridged-deep-learning-approach-for-2","title":"A Skeleton-bridged Deep Learning Approach for Generating Meshesof Complex Topologies from Single RGB Image","date":"2019-04-10","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"wavecyclegan2-time-domain-neural-post-filter","title":"WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation","date":"2019-04-05","arxiv_id":"1904.02892","n_code_links":0,"syntology":null},{"paper":null,"slug":"conditional-adversarial-generative-flow-for","title":"Conditional Adversarial Generative Flow for Controllable Image Synthesis","date":"2019-04-03","arxiv_id":"1904.01782","n_code_links":0,"syntology":null},{"paper":"/paper/a-skeleton-bridged-deep-learning-approach-for","slug":"a-skeleton-bridged-deep-learning-approach-for","title":"A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images","date":"2019-03-12","arxiv_id":"1903.04704","n_code_links":1,"syntology":null},{"paper":null,"slug":"tzk-flow-based-conditional-generative-model","title":"TzK: Flow-Based Conditional Generative Model","date":"2019-02-05","arxiv_id":"1902.01893","n_code_links":0,"syntology":null},{"paper":"/paper/emerging-convolutions-for-generative","slug":"emerging-convolutions-for-generative","title":"Emerging Convolutions for Generative Normalizing Flows","date":"2019-01-30","arxiv_id":"1901.11137","n_code_links":1,"syntology":null},{"paper":"/paper/throttling-malware-families-in-2d","slug":"throttling-malware-families-in-2d","title":"Throttling Malware Families in 2D","date":"2019-01-29","arxiv_id":"1901.10590","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-is-your-mood-when-writing-sexist-tweets","title":"How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques","date":"2019-01-28","arxiv_id":"1902.03089","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-learning-in-unbalanced-and","title":"Semi-supervised learning in unbalanced and heterogeneous networks","date":"2019-01-07","arxiv_id":"1901.01696","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-learning-in-reproducing-kernel-hilbert","title":"Structure learning via unstructured kernel-based M-regression","date":"2019-01-03","arxiv_id":"1901.00615","n_code_links":0,"syntology":null},{"paper":null,"slug":"over-parameterized-deep-neural-networks-have","title":"On the Benefit of Width for Neural Networks: Disappearance of Bad Basins","date":"2018-12-28","arxiv_id":"1812.11039","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-defense-of-image-classification","slug":"adversarial-defense-of-image-classification","title":"Adversarial Defense of Image Classification Using a Variational Auto-Encoder","date":"2018-12-07","arxiv_id":"1812.02891","n_code_links":1,"syntology":null},{"paper":"/paper/feature-selection-with-optimal-coordinate","slug":"feature-selection-with-optimal-coordinate","title":"Feature selection with optimal coordinate ascent (OCA)","date":"2018-11-29","arxiv_id":"1811.12064","n_code_links":1,"syntology":null},{"paper":null,"slug":"stochastic-gradient-descent-optimizes-over","title":"Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks","date":"2018-11-21","arxiv_id":"1811.08888","n_code_links":0,"syntology":null},{"paper":null,"slug":"state-aggregation-learning-from-markov","title":"State Aggregation Learning from Markov Transition Data","date":"2018-11-06","arxiv_id":"1811.02619","n_code_links":0,"syntology":null},{"paper":null,"slug":"task-embedded-coordinate-update-a-realizable","title":"Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-convex Optimization","date":"2018-11-05","arxiv_id":"1811.01587","n_code_links":0,"syntology":null},{"paper":"/paper/waveglow-a-flow-based-generative-network-for","slug":"waveglow-a-flow-based-generative-network-for","title":"WaveGlow: A Flow-based Generative Network for Speech Synthesis","date":"2018-10-31","arxiv_id":"1811.00002","n_code_links":2,"syntology":{"ran":5,"of":7,"n_ran_checked":3,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"5 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; 2 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"optimizing-waiting-thresholds-within-a-state","title":"Optimizing Waiting Thresholds Within A State Machine","date":"2018-10-08","arxiv_id":"1810.03278","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-dictionary-learning-for-approximate","title":"Online Dictionary Learning for Approximate Archetypal Analysis","date":"2018-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"making-emphordinary-least-squares-linear","title":"Linear classifier, least-squares cost function, and outliers","date":"2018-08-28","arxiv_id":"1808.09222","n_code_links":0,"syntology":null},{"paper":"/paper/introvae-introspective-variational","slug":"introvae-introspective-variational","title":"IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis","date":"2018-07-17","arxiv_id":"1807.06358","n_code_links":3,"syntology":null},{"paper":null,"slug":"payoff-control-in-the-iterated-prisoners","title":"Payoff Control in the Iterated Prisoner's Dilemma","date":"2018-07-17","arxiv_id":"1807.06666","n_code_links":0,"syntology":null},{"paper":"/paper/glow-generative-flow-with-invertible-1x1","slug":"glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","arxiv_id":"1807.03039","n_code_links":27,"syntology":{"ran":85,"of":129,"n_ran_checked":73,"n_instrument":12,"unverified":44,"pointer_only":44,"phrase":"85 ran (of which 47 constructed an object rather than computing a result; 73 with no instrument failure: 3 honoured, 0 violated, 70 with no contract checked; 12 where Syntology's instrument failed) · 44 unverified","official":{"repos":["openai/glow"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/reconet-real-time-coherent-video-style","slug":"reconet-real-time-coherent-video-style","title":"ReCoNet: Real-time Coherent Video Style Transfer Network","date":"2018-07-03","arxiv_id":"1807.01197","n_code_links":8,"syntology":null},{"paper":null,"slug":"implicit-regularization-in-nonconvex-1","title":"Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/deep-neural-decision-trees","slug":"deep-neural-decision-trees","title":"Deep Neural Decision Trees","date":"2018-06-19","arxiv_id":"1806.06988","n_code_links":5,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["wOOL/DNDT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/closing-the-generalization-gap-of-adaptive","slug":"closing-the-generalization-gap-of-adaptive","title":"Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks","date":"2018-06-18","arxiv_id":"1806.06763","n_code_links":2,"syntology":null},{"paper":null,"slug":"what-knowledge-is-needed-to-solve-the-rte5","title":"What Knowledge is Needed to Solve the RTE5 Textual Entailment Challenge?","date":"2018-06-10","arxiv_id":"1806.03561","n_code_links":0,"syntology":null},{"paper":"/paper/online-deep-metric-learning","slug":"online-deep-metric-learning","title":"A Multilayer Framework for Online Metric Learning","date":"2018-05-15","arxiv_id":"1805.05510","n_code_links":1,"syntology":null},{"paper":"/paper/robust-blind-deconvolution-via-mirror-descent","slug":"robust-blind-deconvolution-via-mirror-descent","title":"Robust Blind Deconvolution via Mirror Descent","date":"2018-03-21","arxiv_id":"1803.08137","n_code_links":4,"syntology":null},{"paper":"/paper/large-margin-deep-networks-for-classification","slug":"large-margin-deep-networks-for-classification","title":"Large Margin Deep Networks for Classification","date":"2018-03-15","arxiv_id":"1803.05598","n_code_links":2,"syntology":null},{"paper":null,"slug":"an-alternative-view-when-does-sgd-escape","title":"An Alternative View: When Does SGD Escape Local Minima?","date":"2018-02-17","arxiv_id":"1802.06175","n_code_links":0,"syntology":null},{"paper":"/paper/persistence-fisher-kernel-a-riemannian","slug":"persistence-fisher-kernel-a-riemannian","title":"Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams","date":"2018-02-10","arxiv_id":"1802.03569","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-spectral-approach-to-generalization-and","title":"A Spectral Approach to Generalization and Optimization in Neural Networks","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-generative-networks-through","title":"Evaluation of generative networks through their data augmentation capacity","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cnns-are-globally-optimal-given-multi-layer","title":"CNNs are Globally Optimal Given Multi-Layer Support","date":"2017-12-07","arxiv_id":"1712.02501","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-frank-wolfe-and-equilibrium-computation","title":"On Frank-Wolfe and Equilibrium Computation","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"state-space-lstm-models-with-particle-mcmc","title":"State Space LSTM Models with Particle MCMC Inference","date":"2017-11-30","arxiv_id":"1711.11179","n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-regularization-in-nonconvex","title":"Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution","date":"2017-11-28","arxiv_id":"1711.10467","n_code_links":0,"syntology":null},{"paper":null,"slug":"proximal-alternating-direction-network-a","title":"Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework","date":"2017-11-21","arxiv_id":"1711.07653","n_code_links":0,"syntology":null},{"paper":"/paper/wasserstein-auto-encoders","slug":"wasserstein-auto-encoders","title":"Wasserstein Auto-Encoders","date":"2017-11-05","arxiv_id":"1711.01558","n_code_links":14,"syntology":{"ran":6,"of":12,"n_ran_checked":2,"n_instrument":4,"unverified":6,"pointer_only":2,"phrase":"6 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; 4 where Syntology's instrument failed) · 6 unverified","official":{"repos":["tolstikhin/wae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"analysis-of-planar-ornament-patterns-via","title":"Analysis of planar ornament patterns via motif asymmetry assumption and local connections","date":"2017-10-12","arxiv_id":"1710.04623","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-strong-convergence-of-online","title":"Fast and Strong Convergence of Online Learning Algorithms","date":"2017-10-10","arxiv_id":"1710.03600","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-mapping-networks","title":"Generative Adversarial Mapping Networks","date":"2017-09-28","arxiv_id":"1709.09820","n_code_links":0,"syntology":null},{"paper":null,"slug":"order-preserving-abstractive-summarization","title":"Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification","date":"2017-09-16","arxiv_id":"1709.05475","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-vs-diverse-architectures-for","title":"Deep vs. Diverse Architectures for Classification Problems","date":"2017-08-21","arxiv_id":"1708.06347","n_code_links":0,"syntology":null},{"paper":null,"slug":"geometric-enclosing-networks","title":"Geometric Enclosing Networks","date":"2017-08-16","arxiv_id":"1708.04733","n_code_links":0,"syntology":null},{"paper":null,"slug":"follow-the-moving-leader-in-deep-learning","title":"Follow the Moving Leader in Deep Learning","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"restricted-eigenvalue-from-stable-rank-with","title":"Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression","date":"2017-07-25","arxiv_id":"1707.08092","n_code_links":0,"syntology":null}],"record_sha256":"94f12393f7c20ca0c239204b6bb2cf0a13f1cc55eb06ed0d4899ba3bef8dcd67","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}