{"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/quantization/papers/16","list_of":"/task/quantization","task":"Quantization","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":16,"pages_in_order":50,"rows_per_page":100,"rows":[1501,1600],"of":4925,"counts":{"archive_papers_tagged":4925,"with_a_code_link":1596,"where_syntology_ran_a_sample":515,"not_listed_spam_title":0,"listed":4925,"listed_where_code_ran":515,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":452,"every_run_a_failure_of_syntologys_instrument":63,"listed_with_a_run_with_no_instrument_failure":452,"listed_every_run_a_failure_of_syntologys_instrument":63,"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/quantization","prev":"/task/quantization/papers/15","next":"/task/quantization/papers/17","papers":[{"url":"/paper/190503696","slug":"190503696","title":"HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision","date":"2019-04-29","arxiv_id":"1905.03696","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/190503696#ran","syntology_url":"https://syntology.ai/paper/1905.03696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.03696"}},"official":{"repos":["zhen-dong/hawq"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/learning-physical-layer-communication-with","slug":"learning-physical-layer-communication-with","title":"Learning Physical-Layer Communication with Quantized Feedback","date":"2019-04-19","arxiv_id":"1904.09252","repositories_listed":1,"syntology":null},{"url":"/paper/improved-training-of-binary-networks-for","slug":"improved-training-of-binary-networks-for","title":"Improved training of binary networks for human pose estimation and image recognition","date":"2019-04-11","arxiv_id":"1904.05868","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-stochastic-binarization-of-deep","slug":"progressive-stochastic-binarization-of-deep","title":"Progressive Stochastic Binarization of Deep Networks","date":"2019-04-03","arxiv_id":"1904.02205","repositories_listed":1,"syntology":null},{"url":"/paper/variational-inference-with-latent-space","slug":"variational-inference-with-latent-space","title":"Variational Inference with Latent Space Quantization for Adversarial Resilience","date":"2019-03-24","arxiv_id":"1903.09940","repositories_listed":1,"syntology":null},{"url":"/paper/deep-log-likelihood-ratio-quantization","slug":"deep-log-likelihood-ratio-quantization","title":"Deep Log-Likelihood Ratio Quantization","date":"2019-03-11","arxiv_id":"1903.04656","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-generalized-linear-models-with","slug":"accelerating-generalized-linear-models-with","title":"Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)","date":"2019-03-08","arxiv_id":"1903.03404","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-effective-quantization-for","slug":"efficient-and-effective-quantization-for","title":"Focused Quantization for Sparse CNNs","date":"2019-03-07","arxiv_id":"1903.03046","repositories_listed":1,"syntology":null},{"url":"/paper/low-bit-quantization-of-neural-networks-for","slug":"low-bit-quantization-of-neural-networks-for","title":"Low-bit Quantization of Neural Networks for Efficient Inference","date":"2019-02-18","arxiv_id":"1902.06822","repositories_listed":1,"syntology":null},{"url":"/paper/same-same-but-different-recovering-neural","slug":"same-same-but-different-recovering-neural","title":"Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization","date":"2019-02-05","arxiv_id":"1902.01917","repositories_listed":1,"syntology":null},{"url":"/paper/deep-triplet-quantization","slug":"deep-triplet-quantization","title":"Deep Triplet Quantization","date":"2019-02-01","arxiv_id":"1902.00153","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-triplet-quantization#ran","syntology_url":"https://syntology.ai/paper/1902.00153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00153"}},"official":null}},{"url":"/paper/robustness-of-generalized-learning-vector","slug":"robustness-of-generalized-learning-vector","title":"Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks","date":"2019-02-01","arxiv_id":"1902.00577","repositories_listed":1,"syntology":null},{"url":"/paper/learning-sublinear-time-indexing-for-nearest","slug":"learning-sublinear-time-indexing-for-nearest","title":"Learning Space Partitions for Nearest Neighbor Search","date":"2019-01-24","arxiv_id":"1901.08544","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/learning-sublinear-time-indexing-for-nearest#ran","syntology_url":"https://syntology.ai/paper/1901.08544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08544"}},"official":null}},{"url":"/paper/hybrid-coarse-fine-classification-for-head","slug":"hybrid-coarse-fine-classification-for-head","title":"Hybrid coarse-fine classification for head pose estimation","date":"2019-01-21","arxiv_id":"1901.06778","repositories_listed":1,"syntology":null},{"url":"/paper/dsconv-efficient-convolution-operator","slug":"dsconv-efficient-convolution-operator","title":"DSConv: Efficient Convolution Operator","date":"2019-01-07","arxiv_id":"1901.01928","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-at-the-wireless-edge","slug":"machine-learning-at-the-wireless-edge","title":"Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air","date":"2019-01-03","arxiv_id":"1901.00844","repositories_listed":1,"syntology":null},{"url":"/paper/vector-and-line-quantization-for-billion","slug":"vector-and-line-quantization-for-billion","title":"Vector and Line Quantization for Billion-scale Similarity Search on GPUs","date":"2019-01-02","arxiv_id":"1901.00275","repositories_listed":1,"syntology":null},{"url":"/paper/admm-nn-an-algorithm-hardware-co-design","slug":"admm-nn-an-algorithm-hardware-co-design","title":"ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers","date":"2018-12-31","arxiv_id":"1812.11677","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-latent-fingerprint-search","slug":"end-to-end-latent-fingerprint-search","title":"End-to-End Latent Fingerprint Search","date":"2018-12-26","arxiv_id":"1812.10213","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-embedding-methods-in-binary","slug":"exploring-embedding-methods-in-binary","title":"Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces","date":"2018-12-13","arxiv_id":"1812.05705","repositories_listed":1,"syntology":null},{"url":"/paper/proximal-mean-field-for-neural-network","slug":"proximal-mean-field-for-neural-network","title":"Proximal Mean-field for Neural Network Quantization","date":"2018-12-11","arxiv_id":"1812.04353","repositories_listed":1,"syntology":null},{"url":"/paper/trained-rank-pruning-for-efficient-deep","slug":"trained-rank-pruning-for-efficient-deep","title":"Trained Rank Pruning for Efficient Deep Neural Networks","date":"2018-12-06","arxiv_id":"1812.02402","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-dual-vigilance-fuzzy-adaptive","slug":"distributed-dual-vigilance-fuzzy-adaptive","title":"Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence","date":"2018-11-28","arxiv_id":"1901.00794","repositories_listed":1,"syntology":null},{"url":"/paper/fast-high-dimensional-bilateral-and-nonlocal","slug":"fast-high-dimensional-bilateral-and-nonlocal","title":"Fast High-Dimensional Bilateral and Nonlocal Means Filtering","date":"2018-11-06","arxiv_id":"1811.02363","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multiple-description-coding-by-learning","slug":"deep-multiple-description-coding-by-learning","title":"Deep Multiple Description Coding by Learning Scalar Quantization","date":"2018-11-05","arxiv_id":"1811.01504","repositories_listed":1,"syntology":null},{"url":"/paper/qusecnets-quantization-based-defense","slug":"qusecnets-quantization-based-defense","title":"QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks","date":"2018-11-04","arxiv_id":"1811.01437","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-floating-point-for-deep-learning","slug":"rethinking-floating-point-for-deep-learning","title":"Rethinking floating point for deep learning","date":"2018-11-01","arxiv_id":"1811.01721","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_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","sample_list":"/paper/rethinking-floating-point-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/1811.01721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.01721"}},"official":{"repos":["facebookresearch/deepfloat"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-highly-accurate-and-stable-face","slug":"towards-highly-accurate-and-stable-face","title":"Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos","date":"2018-11-01","arxiv_id":"1811.00342","repositories_listed":1,"syntology":null},{"url":"/paper/low-precision-random-fourier-features-for","slug":"low-precision-random-fourier-features-for","title":"Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation","date":"2018-10-31","arxiv_id":"1811.00155","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/low-precision-random-fourier-features-for#ran","syntology_url":"https://syntology.ai/paper/1811.00155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.00155"}},"official":{"repos":["HazyResearch/lp_rffs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-fine-grained-quantization-for","slug":"differentiable-fine-grained-quantization-for","title":"Differentiable Fine-grained Quantization for Deep Neural Network Compression","date":"2018-10-20","arxiv_id":"1810.10351","repositories_listed":1,"syntology":null},{"url":"/paper/relaxed-quantization-for-discretized-neural","slug":"relaxed-quantization-for-discretized-neural","title":"Relaxed Quantization for Discretized Neural Networks","date":"2018-10-03","arxiv_id":"1810.01875","repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-training-of-large-scale-gaussian","slug":"accelerated-training-of-large-scale-gaussian","title":"Large Scale Clustering with Variational EM for Gaussian Mixture Models","date":"2018-10-01","arxiv_id":"1810.00803","repositories_listed":1,"syntology":null},{"url":"/paper/proxquant-quantized-neural-networks-via","slug":"proxquant-quantized-neural-networks-via","title":"ProxQuant: Quantized Neural Networks via Proximal Operators","date":"2018-10-01","arxiv_id":"1810.00861","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/proxquant-quantized-neural-networks-via#ran","syntology_url":"https://syntology.ai/paper/1810.00861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00861"}},"official":{"repos":["allenbai01/ProxQuant"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/nice-noise-injection-and-clamping-estimation","slug":"nice-noise-injection-and-clamping-estimation","title":"NICE: Noise Injection and Clamping Estimation for Neural Network Quantization","date":"2018-09-29","arxiv_id":"1810.00162","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/nice-noise-injection-and-clamping-estimation#ran","syntology_url":"https://syntology.ai/paper/1810.00162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00162"}},"official":{"repos":["Lancer555/NICE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sparsified-sgd-with-memory","slug":"sparsified-sgd-with-memory","title":"Sparsified SGD with Memory","date":"2018-09-20","arxiv_id":"1809.07599","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/sparsified-sgd-with-memory#ran","syntology_url":"https://syntology.ai/paper/1809.07599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.07599"}},"official":{"repos":["epfml/sparsifiedSGD"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-compressive-autoencoder-for-action","slug":"deep-compressive-autoencoder-for-action","title":"Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording","date":"2018-09-14","arxiv_id":"1809.05522","repositories_listed":1,"syntology":null},{"url":"/paper/operations-guided-neural-networks-for-high","slug":"operations-guided-neural-networks-for-high","title":"Operations Guided Neural Networks for High Fidelity Data-To-Text Generation","date":"2018-09-08","arxiv_id":"1809.02735","repositories_listed":1,"syntology":null},{"url":"/paper/deep-priority-hashing","slug":"deep-priority-hashing","title":"Deep Priority Hashing","date":"2018-09-04","arxiv_id":"1809.01238","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compression-from-limited-unlabeled","slug":"learning-compression-from-limited-unlabeled","title":"Learning Compression from Limited Unlabeled Data","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lsq-lower-running-time-and-higher-recall-in","slug":"lsq-lower-running-time-and-higher-recall-in","title":"LSQ++: Lower running time and higher recall in multi-codebook quantization","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-quantized-representations-for","slug":"hierarchical-quantized-representations-for","title":"Hierarchical Quantized Representations for Script Generation","date":"2018-08-28","arxiv_id":"1808.09542","repositories_listed":1,"syntology":null},{"url":"/paper/dnn-feature-map-compression-using-learned","slug":"dnn-feature-map-compression-using-learned","title":"DNN Feature Map Compression using Learned Representation over GF(2)","date":"2018-08-15","arxiv_id":"1808.05285","repositories_listed":1,"syntology":null},{"url":"/paper/lq-nets-learned-quantization-for-highly","slug":"lq-nets-learned-quantization-for-highly","title":"LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks","date":"2018-07-26","arxiv_id":"1807.10029","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/lq-nets-learned-quantization-for-highly#ran","syntology_url":"https://syntology.ai/paper/1807.10029","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10029"}},"official":{"repos":["Microsoft/LQ-Nets"],"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":["official"]}}},{"url":"/paper/deep-attention-based-classification-network","slug":"deep-attention-based-classification-network","title":"Deep attention-based classification network for robust depth prediction","date":"2018-07-11","arxiv_id":"1807.03959","repositories_listed":1,"syntology":null},{"url":"/paper/finn-l-library-extensions-and-design-trade","slug":"finn-l-library-extensions-and-design-trade","title":"FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs","date":"2018-07-11","arxiv_id":"1807.04093","repositories_listed":1,"syntology":null},{"url":"/paper/syq-learning-symmetric-quantization-for","slug":"syq-learning-symmetric-quantization-for","title":"SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks","date":"2018-07-01","arxiv_id":"1807.00301","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-networks-to-enhance","slug":"convolutional-neural-networks-to-enhance","title":"Convolutional Neural Networks to Enhance Coded Speech","date":"2018-06-25","arxiv_id":"1806.09411","repositories_listed":1,"syntology":null},{"url":"/paper/virtual-codec-supervised-re-sampling-network","slug":"virtual-codec-supervised-re-sampling-network","title":"Virtual Codec Supervised Re-Sampling Network for Image Compression","date":"2018-06-22","arxiv_id":"1806.08514","repositories_listed":1,"syntology":null},{"url":"/paper/rgcnn-regularized-graph-cnn-for-point-cloud","slug":"rgcnn-regularized-graph-cnn-for-point-cloud","title":"RGCNN: Regularized Graph CNN for Point Cloud Segmentation","date":"2018-06-08","arxiv_id":"1806.02952","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/rgcnn-regularized-graph-cnn-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/1806.02952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02952"}},"official":null}},{"url":"/paper/deep-image-compression-via-end-to-end","slug":"deep-image-compression-via-end-to-end","title":"Deep Image Compression via End-to-End Learning","date":"2018-06-05","arxiv_id":"1806.01496","repositories_listed":1,"syntology":null},{"url":"/paper/playing-atari-with-six-neurons","slug":"playing-atari-with-six-neurons","title":"Playing Atari with Six Neurons","date":"2018-06-04","arxiv_id":"1806.01363","repositories_listed":1,"syntology":null},{"url":"/paper/two-step-quantization-for-low-bit-neural","slug":"two-step-quantization-for-low-bit-neural","title":"Two-Step Quantization for Low-Bit Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/discrete-factorization-machines-for-fast","slug":"discrete-factorization-machines-for-fast","title":"Discrete Factorization Machines for Fast Feature-based Recommendation","date":"2018-05-06","arxiv_id":"1805.02232","repositories_listed":1,"syntology":null},{"url":"/paper/noise-invariant-frame-selection-a-simple","slug":"noise-invariant-frame-selection-a-simple","title":"Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification","date":"2018-05-03","arxiv_id":"1805.01259","repositories_listed":1,"syntology":null},{"url":"/paper/deep-convolutional-autoencoder-based-lossy","slug":"deep-convolutional-autoencoder-based-lossy","title":"Deep Convolutional AutoEncoder-based Lossy Image Compression","date":"2018-04-25","arxiv_id":"1804.09535","repositories_listed":1,"syntology":null},{"url":"/paper/vision-based-dynamic-offside-line-marker-for","slug":"vision-based-dynamic-offside-line-marker-for","title":"Vision Based Dynamic Offside Line Marker for Soccer Games","date":"2018-04-17","arxiv_id":"1804.06438","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-binary-networks-optimizing-for","slug":"hybrid-binary-networks-optimizing-for","title":"Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory","date":"2018-04-11","arxiv_id":"1804.03867","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-structural-change-in-geographic","slug":"detection-of-structural-change-in-geographic","title":"Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years","date":"2018-03-29","arxiv_id":"1803.11125","repositories_listed":1,"syntology":null},{"url":"/paper/a-quantization-friendly-separable-convolution","slug":"a-quantization-friendly-separable-convolution","title":"A Quantization-Friendly Separable Convolution for MobileNets","date":"2018-03-22","arxiv_id":"1803.08607","repositories_listed":1,"syntology":null},{"url":"/paper/word2bits-quantized-word-vectors","slug":"word2bits-quantized-word-vectors","title":"Word2Bits - Quantized Word Vectors","date":"2018-03-15","arxiv_id":"1803.05651","repositories_listed":1,"syntology":null},{"url":"/paper/high-accuracy-low-precision-training","slug":"high-accuracy-low-precision-training","title":"High-Accuracy Low-Precision Training","date":"2018-03-09","arxiv_id":"1803.03383","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-network-compression-with-single","slug":"deep-neural-network-compression-with-single","title":"Deep Neural Network Compression with Single and Multiple Level Quantization","date":"2018-03-06","arxiv_id":"1803.03289","repositories_listed":1,"syntology":null},{"url":"/paper/expandnet-a-deep-convolutional-neural-network","slug":"expandnet-a-deep-convolutional-neural-network","title":"ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content","date":"2018-03-06","arxiv_id":"1803.02266","repositories_listed":1,"syntology":null},{"url":"/paper/loss-aware-weight-quantization-of-deep","slug":"loss-aware-weight-quantization-of-deep","title":"Loss-aware Weight Quantization of Deep Networks","date":"2018-02-23","arxiv_id":"1802.08635","repositories_listed":1,"syntology":null},{"url":"/paper/rnn-sm-fast-steganalysis-of-voip-streams","slug":"rnn-sm-fast-steganalysis-of-voip-streams","title":"RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network","date":"2018-02-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/piggyback-adapting-a-single-network-to","slug":"piggyback-adapting-a-single-network-to","title":"Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights","date":"2018-01-19","arxiv_id":"1801.06519","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-probability-models-for-deep-image","slug":"conditional-probability-models-for-deep-image","title":"Conditional Probability Models for Deep Image Compression","date":"2018-01-12","arxiv_id":"1801.04260","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-virtual-codec-based-on-deep","slug":"learning-a-virtual-codec-based-on-deep","title":"Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image","date":"2017-12-16","arxiv_id":"1712.05969","repositories_listed":1,"syntology":null},{"url":"/paper/composite-quantization","slug":"composite-quantization","title":"Composite Quantization","date":"2017-12-04","arxiv_id":"1712.00955","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reverse-tone-mapping","slug":"deep-reverse-tone-mapping","title":"Deep reverse tone mapping","date":"2017-11-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-neural-network-pushdown-automaton-model","slug":"the-neural-network-pushdown-automaton-model","title":"The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations","date":"2017-11-15","arxiv_id":"1711.05738","repositories_listed":1,"syntology":null},{"url":"/paper/attacking-binarized-neural-networks","slug":"attacking-binarized-neural-networks","title":"Attacking Binarized Neural Networks","date":"2017-11-01","arxiv_id":"1711.00449","repositories_listed":1,"syntology":{"n":19,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":14,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":19,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/attacking-binarized-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1711.00449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.00449"}},"official":{"repos":["AngusG/cleverhans-attacking-bnns"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":14,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-as-a-mixed-convex-combinatorial","slug":"deep-learning-as-a-mixed-convex-combinatorial","title":"Deep Learning as a Mixed Convex-Combinatorial Optimization Problem","date":"2017-10-31","arxiv_id":"1710.11573","repositories_listed":1,"syntology":null},{"url":"/paper/the-model-of-an-anomaly-detector-for-hilumi","slug":"the-model-of-an-anomaly-detector-for-hilumi","title":"The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization","date":"2017-09-28","arxiv_id":"1709.09883","repositories_listed":1,"syntology":null},{"url":"/paper/joint-maximum-purity-forest-with-application","slug":"joint-maximum-purity-forest-with-application","title":"Joint Maximum Purity Forest with Application to Image Super-Resolution","date":"2017-08-30","arxiv_id":"1708.09200","repositories_listed":1,"syntology":null},{"url":"/paper/learning-accurate-low-bit-deep-neural","slug":"learning-accurate-low-bit-deep-neural","title":"Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization","date":"2017-08-03","arxiv_id":"1708.01001","repositories_listed":1,"syntology":null},{"url":"/paper/monocular-depth-estimation-with-hierarchical","slug":"monocular-depth-estimation-with-hierarchical","title":"Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference","date":"2017-08-02","arxiv_id":"1708.02287","repositories_listed":1,"syntology":null},{"url":"/paper/model-compression-as-constrained-optimization","slug":"model-compression-as-constrained-optimization","title":"Model compression as constrained optimization, with application to neural nets. Part II: quantization","date":"2017-07-13","arxiv_id":"1707.04319","repositories_listed":1,"syntology":null},{"url":"/paper/model-compression-as-constrained-optimization-1","slug":"model-compression-as-constrained-optimization-1","title":"Model compression as constrained optimization, with application to neural nets. Part I: general framework","date":"2017-07-05","arxiv_id":"1707.01209","repositories_listed":1,"syntology":null},{"url":"/paper/bolt-accelerated-data-mining-with-fast-vector","slug":"bolt-accelerated-data-mining-with-fast-vector","title":"Bolt: Accelerated Data Mining with Fast Vector Compression","date":"2017-06-30","arxiv_id":"1706.10283","repositories_listed":1,"syntology":null},{"url":"/paper/shiftcnn-generalized-low-precision","slug":"shiftcnn-generalized-low-precision","title":"ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks","date":"2017-06-07","arxiv_id":"1706.02393","repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-nearest-neighbor-search-with","slug":"accelerated-nearest-neighbor-search-with","title":"Accelerated Nearest Neighbor Search with Quick ADC","date":"2017-04-24","arxiv_id":"1704.07355","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-communication-for-distributed-gradient","slug":"sparse-communication-for-distributed-gradient","title":"Sparse Communication for Distributed Gradient Descent","date":"2017-04-17","arxiv_id":"1704.05021","repositories_listed":1,"syntology":null},{"url":"/paper/a-bag-of-words-equivalent-recurrent-neural","slug":"a-bag-of-words-equivalent-recurrent-neural","title":"A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition","date":"2017-03-23","arxiv_id":"1703.08089","repositories_listed":1,"syntology":null},{"url":"/paper/guetzli-perceptually-guided-jpeg-encoder","slug":"guetzli-perceptually-guided-jpeg-encoder","title":"Guetzli: Perceptually Guided JPEG Encoder","date":"2017-03-13","arxiv_id":"1703.04421","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-large-scale-approximate-nearest","slug":"efficient-large-scale-approximate-nearest","title":"Efficient Large-scale Approximate Nearest Neighbor Search on the GPU","date":"2017-02-20","arxiv_id":"1702.05911","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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) · 0 unverified","sample_list":"/paper/efficient-large-scale-approximate-nearest#ran","syntology_url":"https://syntology.ai/paper/1702.05911","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.05911"}},"official":null}},{"url":"/paper/deep-learning-with-low-precision-by-half-wave","slug":"deep-learning-with-low-precision-by-half-wave","title":"Deep Learning with Low Precision by Half-wave Gaussian Quantization","date":"2017-02-03","arxiv_id":"1702.00953","repositories_listed":1,"syntology":null},{"url":"/paper/fast-supervised-discrete-hashing-and-its","slug":"fast-supervised-discrete-hashing-and-its","title":"Fast Supervised Discrete Hashing and its Analysis","date":"2016-11-30","arxiv_id":"1611.10017","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-networks-with-limited","slug":"recurrent-neural-networks-with-limited","title":"Recurrent Neural Networks With Limited Numerical Precision","date":"2016-11-21","arxiv_id":"1611.07065","repositories_listed":1,"syntology":null},{"url":"/paper/the-zipml-framework-for-training-models-with","slug":"the-zipml-framework-for-training-models-with","title":"The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning","date":"2016-11-16","arxiv_id":"1611.05402","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compact-binary-descriptors-with-1","slug":"learning-compact-binary-descriptors-with-1","title":"Learning compact binary descriptors with unsupervised deep neural networks","date":"2016-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/web-scale-image-clustering-revisited","slug":"web-scale-image-clustering-revisited","title":"Web-Scale Image Clustering Revisited","date":"2015-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-client-side-chinesejapanese","slug":"lightweight-client-side-chinesejapanese","title":"Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online Learning","date":"2014-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cartesian-k-means","slug":"cartesian-k-means","title":"Cartesian K-Means","date":"2013-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-statistical-classification-of","slug":"efficient-statistical-classification-of","title":"Efficient statistical classification of satellite measurements","date":"2012-02-10","arxiv_id":"1202.2194","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-recognition-with-a-vocabulary-tree","slug":"scalable-recognition-with-a-vocabulary-tree","title":"Scalable Recognition with a Vocabulary Tree","date":"2006-06-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"an-end-to-end-dnn-inference-framework-for-the","title":"An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC","date":"2025-07-18","arxiv_id":"2507.13736","repositories_listed":0,"syntology":null},{"url":null,"slug":"angle-estimation-of-a-single-source-with","title":"Angle Estimation of a Single Source with Massive Uniform Circular Arrays","date":"2025-07-17","arxiv_id":"2507.13086","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-specific-audio-coding-for-machines","title":"Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine","date":"2025-07-17","arxiv_id":"2507.12701","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-rank-reduction-a-communications","title":"Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications","date":"2025-07-15","arxiv_id":"2507.11183","repositories_listed":0,"syntology":null}],"record_sha256":"573e484de06abb0da9a71832d26532eb3a024926affce7d459d4870269dd9260","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}