{"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/14","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":14,"pages_in_order":50,"rows_per_page":100,"rows":[1301,1400],"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/13","next":"/task/quantization/papers/15","papers":[{"url":"/paper/conditional-coding-and-variable-bitrate-for","slug":"conditional-coding-and-variable-bitrate-for","title":"Conditional Coding and Variable Bitrate for Practical Learned Video Coding","date":"2021-04-19","arxiv_id":"2104.09103","repositories_listed":1,"syntology":null},{"url":"/paper/filtering-empty-camera-trap-images-in","slug":"filtering-empty-camera-trap-images-in","title":"Filtering Empty Camera Trap Images in Embedded Systems","date":"2021-04-18","arxiv_id":"2104.08859","repositories_listed":1,"syntology":null},{"url":"/paper/random-and-adversarial-bit-error-robustness","slug":"random-and-adversarial-bit-error-robustness","title":"Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators","date":"2021-04-16","arxiv_id":"2104.08323","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-learning-systems-with-first-order","slug":"distributed-learning-systems-with-first-order","title":"Distributed Learning Systems with First-order Methods","date":"2021-04-12","arxiv_id":"2104.05245","repositories_listed":1,"syntology":null},{"url":"/paper/learned-transform-compression-with-optimized","slug":"learned-transform-compression-with-optimized","title":"Learned transform compression with optimized entropy encoding","date":"2021-04-07","arxiv_id":"2104.03305","repositories_listed":1,"syntology":null},{"url":"/paper/quantized-gromov-wasserstein","slug":"quantized-gromov-wasserstein","title":"Quantized Gromov-Wasserstein","date":"2021-04-05","arxiv_id":"2104.02013","repositories_listed":1,"syntology":null},{"url":"/paper/network-quantization-with-element-wise","slug":"network-quantization-with-element-wise","title":"Network Quantization with Element-wise Gradient Scaling","date":"2021-04-02","arxiv_id":"2104.00903","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/network-quantization-with-element-wise#ran","syntology_url":"https://syntology.ai/paper/2104.00903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00903"}},"official":{"repos":["cvlab-yonsei/EWGS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-multi-bit-quantized-and-binarized","slug":"training-multi-bit-quantized-and-binarized","title":"Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric Quantizer","date":"2021-04-01","arxiv_id":"2104.00210","repositories_listed":1,"syntology":null},{"url":"/paper/q-asr-integer-only-zero-shot-quantization-for","slug":"q-asr-integer-only-zero-shot-quantization-for","title":"Integer-only Zero-shot Quantization for Efficient Speech Recognition","date":"2021-03-31","arxiv_id":"2103.16827","repositories_listed":1,"syntology":null},{"url":"/paper/rangedet-in-defense-of-range-view-for-lidar","slug":"rangedet-in-defense-of-range-view-for-lidar","title":"RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection","date":"2021-03-18","arxiv_id":"2103.10039","repositories_listed":1,"syntology":null},{"url":"/paper/multi-prize-lottery-ticket-hypothesis-finding-1","slug":"multi-prize-lottery-ticket-hypothesis-finding-1","title":"Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network","date":"2021-03-17","arxiv_id":"2103.09377","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-learning-and-democratic","slug":"distributed-learning-and-democratic","title":"Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget","date":"2021-03-13","arxiv_id":"2103.07578","repositories_listed":1,"syntology":null},{"url":"/paper/learning-statistical-texture-for-semantic","slug":"learning-statistical-texture-for-semantic","title":"Learning Statistical Texture for Semantic Segmentation","date":"2021-03-06","arxiv_id":"2103.04133","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 4 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) · 2 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-statistical-texture-for-semantic#ran","syntology_url":"https://syntology.ai/paper/2103.04133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04133"}},"official":{"repos":["lanyunzhu99/Learning-Statistical-Texture-for-Semantic-Segmentation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/environmental-sound-classification-on-the","slug":"environmental-sound-classification-on-the","title":"Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices","date":"2021-03-05","arxiv_id":"2103.03483","repositories_listed":1,"syntology":null},{"url":"/paper/pufferfish-communication-efficient-models-at","slug":"pufferfish-communication-efficient-models-at","title":"Pufferfish: Communication-efficient Models At No Extra Cost","date":"2021-03-05","arxiv_id":"2103.03936","repositories_listed":1,"syntology":null},{"url":"/paper/self-distribution-binary-neural-networks","slug":"self-distribution-binary-neural-networks","title":"Self-Distribution Binary Neural Networks","date":"2021-03-03","arxiv_id":"2103.02394","repositories_listed":1,"syntology":null},{"url":"/paper/mixed-precision-quantization-and-parallel","slug":"mixed-precision-quantization-and-parallel","title":"Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces","date":"2021-02-22","arxiv_id":"2102.11221","repositories_listed":1,"syntology":null},{"url":"/paper/ps-and-qs-quantization-aware-pruning-for","slug":"ps-and-qs-quantization-aware-pruning-for","title":"Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference","date":"2021-02-22","arxiv_id":"2102.11289","repositories_listed":1,"syntology":null},{"url":"/paper/bsq-exploring-bit-level-sparsity-for-mixed-1","slug":"bsq-exploring-bit-level-sparsity-for-mixed-1","title":"BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization","date":"2021-02-20","arxiv_id":"2102.10462","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bsq-exploring-bit-level-sparsity-for-mixed-1#ran","syntology_url":"https://syntology.ai/paper/2102.10462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10462"}},"official":{"repos":["yanghr/BSQ"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fat-learning-low-bitwidth-parametric","slug":"fat-learning-low-bitwidth-parametric","title":"FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation","date":"2021-02-15","arxiv_id":"2102.07444","repositories_listed":1,"syntology":null},{"url":"/paper/confounding-tradeoffs-for-neural-network","slug":"confounding-tradeoffs-for-neural-network","title":"Confounding Tradeoffs for Neural Network Quantization","date":"2021-02-12","arxiv_id":"2102.06366","repositories_listed":1,"syntology":null},{"url":"/paper/the-distributed-discrete-gaussian-mechanism","slug":"the-distributed-discrete-gaussian-mechanism","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","date":"2021-02-12","arxiv_id":"2102.06387","repositories_listed":1,"syntology":null},{"url":"/paper/visualizing-hierarchies-in-scrna-seq-data","slug":"visualizing-hierarchies-in-scrna-seq-data","title":"Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder","date":"2021-02-11","arxiv_id":"2102.05892","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-universal-transformation-of-data","slug":"on-the-universal-transformation-of-data","title":"On the Universal Transformation of Data-Driven Models to Control Systems","date":"2021-02-09","arxiv_id":"2102.04722","repositories_listed":1,"syntology":null},{"url":"/paper/refining-a-nearest-neighbor-graph-for-a","slug":"refining-a-nearest-neighbor-graph-for-a","title":"Refining a -nearest neighbor graph for a computationally efficient spectral clustering","date":"2021-02-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/compressed-object-detection","slug":"compressed-object-detection","title":"Compressed Object Detection","date":"2021-02-04","arxiv_id":"2102.02896","repositories_listed":1,"syntology":null},{"url":"/paper/fixed-point-quantization-of-convolutional","slug":"fixed-point-quantization-of-convolutional","title":"Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms","date":"2021-02-03","arxiv_id":"2102.02147","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-quantized-neural-networks-on","slug":"benchmarking-quantized-neural-networks-on","title":"Benchmarking Quantized Neural Networks on FPGAs with FINN","date":"2021-02-02","arxiv_id":"2102.01341","repositories_listed":1,"syntology":null},{"url":"/paper/fedzip-a-compression-framework-for","slug":"fedzip-a-compression-framework-for","title":"FEDZIP: A Compression Framework for Communication-Efficient Federated Learning","date":"2021-02-02","arxiv_id":"2102.01593","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-cache-boundness-of-ml-operators","slug":"understanding-cache-boundness-of-ml-operators","title":"Understanding Cache Boundness of ML Operators on ARM Processors","date":"2021-02-01","arxiv_id":"2102.00932","repositories_listed":1,"syntology":null},{"url":"/paper/error-diffusion-halftoning-against","slug":"error-diffusion-halftoning-against","title":"Error Diffusion Halftoning Against Adversarial Examples","date":"2021-01-23","arxiv_id":"2101.09451","repositories_listed":1,"syntology":null},{"url":"/paper/sparsednn-fast-sparse-deep-learning-inference","slug":"sparsednn-fast-sparse-deep-learning-inference","title":"SparseDNN: Fast Sparse Deep Learning Inference on CPUs","date":"2021-01-20","arxiv_id":"2101.07948","repositories_listed":1,"syntology":null},{"url":"/paper/fbgemm-enabling-high-performance-low","slug":"fbgemm-enabling-high-performance-low","title":"FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference","date":"2021-01-13","arxiv_id":"2101.05615","repositories_listed":1,"syntology":null},{"url":"/paper/binary-ttc-a-temporal-geofence-for-autonomous","slug":"binary-ttc-a-temporal-geofence-for-autonomous","title":"Binary TTC: A Temporal Geofence for Autonomous Navigation","date":"2021-01-12","arxiv_id":"2101.04777","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/binary-ttc-a-temporal-geofence-for-autonomous#ran","syntology_url":"https://syntology.ai/paper/2101.04777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.04777"}},"official":{"repos":["NVlabs/BiTTC"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/computational-data-analysis-for-first","slug":"computational-data-analysis-for-first","title":"Computational data analysis for first quantization estimation on JPEG double compressed images","date":"2021-01-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/who-s-a-good-boy-reinforcing-canine-behavior","slug":"who-s-a-good-boy-reinforcing-canine-behavior","title":"Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine Learning","date":"2021-01-07","arxiv_id":"2101.02380","repositories_listed":1,"syntology":null},{"url":"/paper/improving-neural-network-efficiency-via-post","slug":"improving-neural-network-efficiency-via-post","title":"Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rangedet-in-defense-of-range-view-for-lidar-1","slug":"rangedet-in-defense-of-range-view-for-lidar-1","title":"RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uniformity-in-heterogeneity-diving-deep-into-1","slug":"uniformity-in-heterogeneity-diving-deep-into-1","title":"Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd Counting","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/waveq-gradient-based-deep-quantization-of","slug":"waveq-gradient-based-deep-quantization-of","title":"WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/binarybert-pushing-the-limit-of-bert","slug":"binarybert-pushing-the-limit-of-bert","title":"BinaryBERT: Pushing the Limit of BERT Quantization","date":"2020-12-31","arxiv_id":"2012.15701","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/binarybert-pushing-the-limit-of-bert#ran","syntology_url":"https://syntology.ai/paper/2012.15701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.15701"}},"official":{"repos":["huawei-noah/Pretrained-Language-Model"],"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","unlocated"]}}},{"url":"/paper/a-memory-efficient-baseline-for-open-domain","slug":"a-memory-efficient-baseline-for-open-domain","title":"A Memory Efficient Baseline for Open Domain Question Answering","date":"2020-12-30","arxiv_id":"2012.15156","repositories_listed":1,"syntology":null},{"url":"/paper/comprehensive-graph-conditional-similarity","slug":"comprehensive-graph-conditional-similarity","title":"Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing","date":"2020-12-25","arxiv_id":"2012.13538","repositories_listed":1,"syntology":null},{"url":"/paper/fractrain-fractionally-squeezing-bit-savings-1","slug":"fractrain-fractionally-squeezing-bit-savings-1","title":"FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training","date":"2020-12-24","arxiv_id":"2012.13113","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/fractrain-fractionally-squeezing-bit-savings-1#ran","syntology_url":"https://syntology.ai/paper/2012.13113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.13113"}},"official":{"repos":["RICE-EIC/FracTrain"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/efficient-cnn-lstm-based-image-captioning","slug":"efficient-cnn-lstm-based-image-captioning","title":"Efficient CNN-LSTM based Image Captioning using Neural Network Compression","date":"2020-12-17","arxiv_id":"2012.09708","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-verification-of-quantized-neural","slug":"scalable-verification-of-quantized-neural","title":"Scalable Verification of Quantized Neural Networks (Technical Report)","date":"2020-12-15","arxiv_id":"2012.08185","repositories_listed":1,"syntology":null},{"url":"/paper/decoar-2-0-deep-contextualized-acoustic","slug":"decoar-2-0-deep-contextualized-acoustic","title":"DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization","date":"2020-12-11","arxiv_id":"2012.06659","repositories_listed":1,"syntology":null},{"url":"/paper/robustness-and-transferability-of-universal","slug":"robustness-and-transferability-of-universal","title":"Robustness and Transferability of Universal Attacks on Compressed Models","date":"2020-12-10","arxiv_id":"2012.06024","repositories_listed":1,"syntology":null},{"url":"/paper/parallel-blockwise-knowledge-distillation-for","slug":"parallel-blockwise-knowledge-distillation-for","title":"Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression","date":"2020-12-05","arxiv_id":"2012.03096","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-model-compression-via-label","slug":"reliable-model-compression-via-label","title":"Going Beyond Classification Accuracy Metrics in Model Compression","date":"2020-12-03","arxiv_id":"2012.01604","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-cnn-based-primary-quantization","slug":"boosting-cnn-based-primary-quantization","title":"Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture","date":"2020-12-01","arxiv_id":"2012.00468","repositories_listed":1,"syntology":null},{"url":"/paper/fast-adversarial-robustness-certification-of","slug":"fast-adversarial-robustness-certification-of","title":"Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/kd-lib-a-pytorch-library-for-knowledge","slug":"kd-lib-a-pytorch-library-for-knowledge","title":"KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization","date":"2020-11-30","arxiv_id":"2011.14691","repositories_listed":1,"syntology":null},{"url":"/paper/fully-quantized-image-super-resolution","slug":"fully-quantized-image-super-resolution","title":"Fully Quantized Image Super-Resolution Networks","date":"2020-11-29","arxiv_id":"2011.14265","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-evaluation-of-deep-learning-model","slug":"empirical-evaluation-of-deep-learning-model","title":"Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder","date":"2020-11-20","arxiv_id":"2011.10469","repositories_listed":1,"syntology":null},{"url":"/paper/hawqv3-dyadic-neural-network-quantization","slug":"hawqv3-dyadic-neural-network-quantization","title":"HAWQV3: Dyadic Neural Network Quantization","date":"2020-11-20","arxiv_id":"2011.10680","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/hawqv3-dyadic-neural-network-quantization#ran","syntology_url":"https://syntology.ai/paper/2011.10680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10680"}},"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/pams-quantized-super-resolution-via-1","slug":"pams-quantized-super-resolution-via-1","title":"PAMS: Quantized Super-Resolution via Parameterized Max Scale","date":"2020-11-09","arxiv_id":"2011.04212","repositories_listed":1,"syntology":null},{"url":"/paper/a-greedy-algorithm-for-quantizing-neural","slug":"a-greedy-algorithm-for-quantizing-neural","title":"A Greedy Algorithm for Quantizing Neural Networks","date":"2020-10-29","arxiv_id":"2010.15979","repositories_listed":1,"syntology":null},{"url":"/paper/permute-quantize-and-fine-tune-efficient","slug":"permute-quantize-and-fine-tune-efficient","title":"Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks","date":"2020-10-29","arxiv_id":"2010.15703","repositories_listed":1,"syntology":null},{"url":"/paper/shiftaddnet-a-hardware-inspired-deep-network","slug":"shiftaddnet-a-hardware-inspired-deep-network","title":"ShiftAddNet: A Hardware-Inspired Deep Network","date":"2020-10-24","arxiv_id":"2010.12785","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/shiftaddnet-a-hardware-inspired-deep-network#ran","syntology_url":"https://syntology.ai/paper/2010.12785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12785"}},"official":{"repos":["RICE-EIC/ShiftAddNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-gradient-quantization-for-data","slug":"adaptive-gradient-quantization-for-data","title":"Adaptive Gradient Quantization for Data-Parallel SGD","date":"2020-10-23","arxiv_id":"2010.12460","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-gradient-quantization-for-data#ran","syntology_url":"https://syntology.ai/paper/2010.12460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12460"}},"official":{"repos":["tabrizian/learning-to-quantize"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/linearly-converging-error-compensated-sgd","slug":"linearly-converging-error-compensated-sgd","title":"Linearly Converging Error Compensated SGD","date":"2020-10-23","arxiv_id":"2010.12292","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/linearly-converging-error-compensated-sgd#ran","syntology_url":"https://syntology.ai/paper/2010.12292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12292"}},"official":{"repos":["eduardgorbunov/ef_sigma_k"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/on-resource-efficient-bayesian-network","slug":"on-resource-efficient-bayesian-network","title":"On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks","date":"2020-10-22","arxiv_id":"2010.11773","repositories_listed":1,"syntology":null},{"url":"/paper/bi-real-net-v2-rethinking-non-linearity-for-1-1","slug":"bi-real-net-v2-rethinking-non-linearity-for-1-1","title":"FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond","date":"2020-10-19","arxiv_id":"2010.09294","repositories_listed":1,"syntology":null},{"url":"/paper/once-quantized-for-all-progressively-1","slug":"once-quantized-for-all-progressively-1","title":"Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search","date":"2020-10-09","arxiv_id":"2010.04354","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-transformers-with-approximate-1","slug":"optimizing-transformers-with-approximate-1","title":"AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models","date":"2020-10-07","arxiv_id":"2010.03688","repositories_listed":1,"syntology":null},{"url":"/paper/faster-binary-embeddings-for-preserving","slug":"faster-binary-embeddings-for-preserving","title":"Faster Binary Embeddings for Preserving Euclidean Distances","date":"2020-10-01","arxiv_id":"2010.00712","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/faster-binary-embeddings-for-preserving#ran","syntology_url":"https://syntology.ai/paper/2010.00712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.00712"}},"official":{"repos":["jayzhang0727/Faster-Binary-Embeddings-for-Preserving-Euclidean-Distances"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/recursive-csi-quantization-of-time-correlated","slug":"recursive-csi-quantization-of-time-correlated","title":"Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification","date":"2020-09-28","arxiv_id":"2009.13560","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-improve-image-compression-without","slug":"learning-to-improve-image-compression-without","title":"Learning to Improve Image Compression without Changing the Standard Decoder","date":"2020-09-27","arxiv_id":"2009.12927","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-debanding-filter","slug":"adaptive-debanding-filter","title":"Adaptive Debanding Filter","date":"2020-09-22","arxiv_id":"2009.10804","repositories_listed":1,"syntology":null},{"url":"/paper/searching-for-low-bit-weights-in-quantized","slug":"searching-for-low-bit-weights-in-quantized","title":"Searching for Low-Bit Weights in Quantized Neural Networks","date":"2020-09-18","arxiv_id":"2009.08695","repositories_listed":1,"syntology":null},{"url":"/paper/approximate-spectral-clustering-using-both","slug":"approximate-spectral-clustering-using-both","title":"Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas","date":"2020-09-15","arxiv_id":"2009.07101","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-single-model-with-a-wide-range-of","slug":"learning-a-single-model-with-a-wide-range-of","title":"Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal","date":"2020-09-15","arxiv_id":"2009.06912","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-beats-fast-classification-base-on-sparse","slug":"ecg-beats-fast-classification-base-on-sparse","title":"ECG Beats Fast Classification Base on Sparse Dictionaries","date":"2020-09-08","arxiv_id":"2009.03792","repositories_listed":1,"syntology":null},{"url":"/paper/algorithm-and-vlsi-design-for-1-bit-data","slug":"algorithm-and-vlsi-design-for-1-bit-data","title":"Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDM","date":"2020-09-04","arxiv_id":"2009.02068","repositories_listed":1,"syntology":null},{"url":"/paper/an-integrated-approach-to-produce-robust","slug":"an-integrated-approach-to-produce-robust","title":"An Integrated Approach to Produce Robust Models with High Efficiency","date":"2020-08-31","arxiv_id":"2008.13305","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-beats-classification-via-online-sparse","slug":"ecg-beats-classification-via-online-sparse","title":"ECG beats classification via online sparse dictionary and time pyramid matching","date":"2020-08-15","arxiv_id":"2008.06672","repositories_listed":1,"syntology":null},{"url":"/paper/profit-a-novel-training-method-for-sub-4-bit","slug":"profit-a-novel-training-method-for-sub-4-bit","title":"PROFIT: A Novel Training Method for sub-4-bit MobileNet Models","date":"2020-08-11","arxiv_id":"2008.04693","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_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","sample_list":"/paper/profit-a-novel-training-method-for-sub-4-bit#ran","syntology_url":"https://syntology.ai/paper/2008.04693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.04693"}},"official":{"repos":["EunhyeokPark/PROFIT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-sockeye-2-neural-machine-translation","slug":"the-sockeye-2-neural-machine-translation","title":"The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020","date":"2020-08-11","arxiv_id":"2008.04885","repositories_listed":1,"syntology":null},{"url":"/paper/trend-transferability-based-robust-ensemble","slug":"trend-transferability-based-robust-ensemble","title":"TREND: Transferability based Robust ENsemble Design","date":"2020-08-04","arxiv_id":"2008.01524","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transferring-quantization","slug":"deep-transferring-quantization","title":"Deep Transferring Quantization","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/geometric-total-variation-for-image","slug":"geometric-total-variation-for-image","title":"Geometric Total Variation for Image Vectorization, Zooming and Pixel Art Depixelizing","date":"2020-07-31","arxiv_id":"2007.15933","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-learning-of-compressible-features","slug":"end-to-end-learning-of-compressible-features","title":"End-to-end Learning of Compressible Features","date":"2020-07-23","arxiv_id":"2007.11797","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-vulnerabilities-of-deep-neural","slug":"exploiting-vulnerabilities-of-deep-neural","title":"Exploiting vulnerabilities of deep neural networks for privacy protection","date":"2020-07-19","arxiv_id":"2007.09766","repositories_listed":1,"syntology":null},{"url":"/paper/resolution-switchable-networks-for-runtime","slug":"resolution-switchable-networks-for-runtime","title":"Resolution Switchable Networks for Runtime Efficient Image Recognition","date":"2020-07-19","arxiv_id":"2007.09558","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/resolution-switchable-networks-for-runtime#ran","syntology_url":"https://syntology.ai/paper/2007.09558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09558"}},"official":{"repos":["yikaiw/RS-Nets"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/device-robust-acoustic-scene-classification","slug":"device-robust-acoustic-scene-classification","title":"Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation","date":"2020-07-16","arxiv_id":"2007.08389","repositories_listed":1,"syntology":null},{"url":"/paper/channel-level-variable-quantization-network","slug":"channel-level-variable-quantization-network","title":"Channel-Level Variable Quantization Network for Deep Image Compression","date":"2020-07-15","arxiv_id":"2007.12619","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/channel-level-variable-quantization-network#ran","syntology_url":"https://syntology.ai/paper/2007.12619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12619"}},"official":{"repos":["zzs1994/CVQN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/finding-non-uniform-quantization-schemes","slug":"finding-non-uniform-quantization-schemes","title":"Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes","date":"2020-07-15","arxiv_id":"2007.07743","repositories_listed":1,"syntology":null},{"url":"/paper/aqd-towards-accurate-quantized-object","slug":"aqd-towards-accurate-quantized-object","title":"AQD: Towards Accurate Fully-Quantized Object Detection","date":"2020-07-14","arxiv_id":"2007.06919","repositories_listed":1,"syntology":null},{"url":"/paper/fracbits-mixed-precision-quantization-via","slug":"fracbits-mixed-precision-quantization-via","title":"FracBits: Mixed Precision Quantization via Fractional Bit-Widths","date":"2020-07-04","arxiv_id":"2007.02017","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":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fracbits-mixed-precision-quantization-via#ran","syntology_url":"https://syntology.ai/paper/2007.02017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02017"}},"official":null}},{"url":"/paper/deep-pensieve-a-deep-learning-framework-based","slug":"deep-pensieve-a-deep-learning-framework-based","title":"Deep PeNSieve: A deep learning framework based on the posit number system","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/easyquant-post-training-quantization-via","slug":"easyquant-post-training-quantization-via","title":"EasyQuant: Post-training Quantization via Scale Optimization","date":"2020-06-30","arxiv_id":"2006.16669","repositories_listed":1,"syntology":null},{"url":"/paper/making-densepose-fast-and-light","slug":"making-densepose-fast-and-light","title":"Making DensePose fast and light","date":"2020-06-26","arxiv_id":"2006.15190","repositories_listed":1,"syntology":null},{"url":"/paper/on-mitigating-random-and-adversarial-bit","slug":"on-mitigating-random-and-adversarial-bit","title":"Bit Error Robustness for Energy-Efficient DNN Accelerators","date":"2020-06-24","arxiv_id":"2006.13977","repositories_listed":1,"syntology":null},{"url":"/paper/multi-class-uncertainty-calibration-via","slug":"multi-class-uncertainty-calibration-via","title":"Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning","date":"2020-06-23","arxiv_id":"2006.13092","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-integer-arithmetic-only","slug":"efficient-integer-arithmetic-only","title":"Efficient Integer-Arithmetic-Only Convolutional Neural Networks","date":"2020-06-21","arxiv_id":"2006.11735","repositories_listed":1,"syntology":null},{"url":"/paper/statassist-gradboost-a-study-on-optimal-int8","slug":"statassist-gradboost-a-study-on-optimal-int8","title":"FrostNet: Towards Quantization-Aware Network Architecture Search","date":"2020-06-17","arxiv_id":"2006.09679","repositories_listed":1,"syntology":null},{"url":"/paper/apq-joint-search-for-network-architecture-1","slug":"apq-joint-search-for-network-architecture-1","title":"APQ: Joint Search for Network Architecture, Pruning and Quantization Policy","date":"2020-06-15","arxiv_id":"2006.08509","repositories_listed":1,"syntology":null},{"url":"/paper/hyperflow-representing-3d-objects-as-surfaces","slug":"hyperflow-representing-3d-objects-as-surfaces","title":"HyperFlow: Representing 3D Objects as Surfaces","date":"2020-06-15","arxiv_id":"2006.08710","repositories_listed":1,"syntology":null},{"url":"/paper/improving-post-training-neural-quantization","slug":"improving-post-training-neural-quantization","title":"Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming","date":"2020-06-14","arxiv_id":"2006.10518","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/improving-post-training-neural-quantization#ran","syntology_url":"https://syntology.ai/paper/2006.10518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10518"}},"official":{"repos":["itayhubara/CalibTIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}}],"record_sha256":"b85bf5315d55bef2fb597c5b3e1de0f6940b04518a547565c7f82d53988cf233","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}