{"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/38","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":38,"pages_in_order":50,"rows_per_page":100,"rows":[3701,3800],"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/37","next":"/task/quantization/papers/39","papers":[{"url":null,"slug":"risk-assessment-for-connected-vehicles-under","title":"Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks","date":"2021-09-03","arxiv_id":"2109.01553","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-of-generative-adversarial","title":"Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study","date":"2021-08-31","arxiv_id":"2108.13996","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-convolutional-neural-networks","title":"Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations","date":"2021-08-31","arxiv_id":"2109.00095","repositories_listed":0,"syntology":null},{"url":null,"slug":"4-bit-quantization-of-lstm-based-speech","title":"4-bit Quantization of LSTM-based Speech Recognition Models","date":"2021-08-27","arxiv_id":"2108.12074","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-approach-to-the-temporal","title":"A Quantitative Approach To The Temporal Dependency in Video Coding","date":"2021-08-26","arxiv_id":"2108.11586","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-adaptive-transmission-for-distributed","title":"On Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks with Limited Fusion Center Feedback","date":"2021-08-23","arxiv_id":"2108.10358","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-acceleration-of-deep-neural-network","title":"On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing","date":"2021-08-23","arxiv_id":"2108.10101","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-distortion-comparison-of-a-few-gradient","title":"Rate distortion comparison of a few gradient quantizers","date":"2021-08-23","arxiv_id":"2108.09899","repositories_listed":0,"syntology":null},{"url":null,"slug":"integer-arithmetic-only-certified-robustness","title":"Integer-arithmetic-only Certified Robustness for Quantized Neural Networks","date":"2021-08-21","arxiv_id":"2108.09413","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconfigurable-co-processor-architecture-with","title":"Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks","date":"2021-08-21","arxiv_id":"2109.03040","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-backdoors-to-deep-learning","title":"Quantization Backdoors to Deep Learning Commercial Frameworks","date":"2021-08-20","arxiv_id":"2108.09187","repositories_listed":0,"syntology":null},{"url":null,"slug":"deployment-of-deep-neural-networks-for-object","title":"Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization","date":"2021-08-18","arxiv_id":"2108.08166","repositories_listed":0,"syntology":null},{"url":null,"slug":"verifying-low-dimensional-input-neural","title":"Verifying Low-dimensional Input Neural Networks via Input Quantization","date":"2021-08-18","arxiv_id":"2108.07961","repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-aware-quantization","title":"Distance-aware Quantization","date":"2021-08-16","arxiv_id":"2108.06983","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficiency-maximization-precoding-for","title":"Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems","date":"2021-08-06","arxiv_id":"2108.03048","repositories_listed":0,"syntology":null},{"url":null,"slug":"bifocal-neural-asr-exploiting-keyword","title":"Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization","date":"2021-08-03","arxiv_id":"2108.01704","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-digital-los-mimo-with-low-precision","title":"All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion","date":"2021-08-02","arxiv_id":"2108.01147","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-recurrent-neural-networks","title":"MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks","date":"2021-08-02","arxiv_id":"2108.01192","repositories_listed":0,"syntology":null},{"url":null,"slug":"dq-sgd-dynamic-quantization-in-sgd-for","title":"DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning","date":"2021-07-30","arxiv_id":"2107.14575","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-compression-network-with","title":"Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search","date":"2021-07-30","arxiv_id":"2107.14415","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-morphometry-of-closed-implicit-surfaces","title":"Local Morphometry of Closed, Implicit Surfaces","date":"2021-07-29","arxiv_id":"2108.04354","repositories_listed":0,"syntology":null},{"url":null,"slug":"quped-quantized-personalization-via","title":"QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning","date":"2021-07-29","arxiv_id":"2107.13892","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-joint-transmission-for-cloud-radio","title":"Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity","date":"2021-07-29","arxiv_id":"2107.13819","repositories_listed":0,"syntology":null},{"url":null,"slug":"marvin-multiple-arithmetic-resolutions","title":"Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs","date":"2021-07-28","arxiv_id":"2107.13490","repositories_listed":0,"syntology":null},{"url":null,"slug":"dv-det-efficient-3d-point-cloud-object","title":"DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization","date":"2021-07-27","arxiv_id":"2107.12707","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-estimation-and-pilot-signal","title":"Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems","date":"2021-07-26","arxiv_id":"2107.11958","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-distribution-generation","title":"High-Dimensional Distribution Generation Through Deep Neural Networks","date":"2021-07-26","arxiv_id":"2107.12466","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-energy-efficient-quantized-deep","title":"HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification","date":"2021-07-26","arxiv_id":"2107.11979","repositories_listed":0,"syntology":null},{"url":null,"slug":"finite-bit-quantization-for-distributed","title":"Finite-Bit Quantization For Distributed Algorithms With Linear Convergence","date":"2021-07-23","arxiv_id":"2107.11304","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-ternary-quantization","title":"Pruning Ternary Quantization","date":"2021-07-23","arxiv_id":"2107.10998","repositories_listed":0,"syntology":null},{"url":null,"slug":"harp-net-hyper-autoencoded-reconstruction","title":"HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding","date":"2021-07-22","arxiv_id":"2107.10843","repositories_listed":0,"syntology":null},{"url":null,"slug":"kramers-kronig-receiver-combined-with-digital","title":"Kramers-Kronig Receiver Combined With Digital Resolution Enhancer","date":"2021-07-22","arxiv_id":"2107.10626","repositories_listed":0,"syntology":null},{"url":null,"slug":"crew-computation-reuse-and-efficient-weight","title":"CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs","date":"2021-07-20","arxiv_id":"2107.09408","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-double-compression-in-mpeg-4","title":"DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms","date":"2021-07-19","arxiv_id":"2107.08939","repositories_listed":0,"syntology":null},{"url":null,"slug":"support-recovery-in-universal-one-bit","title":"Support Recovery in Universal One-bit Compressed Sensing","date":"2021-07-19","arxiv_id":"2107.09091","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-high-performance-adaptive-quantization","title":"A High-Performance Adaptive Quantization Approach for Edge CNN Applications","date":"2021-07-18","arxiv_id":"2107.08382","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-to-ternary-hash-codes-by","title":"Deep Learning to Ternary Hash Codes by Continuation","date":"2021-07-16","arxiv_id":"2107.07987","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-set-of-vectors-search","title":"Efficient Approximate Search for Sets of Vectors","date":"2021-07-14","arxiv_id":"2107.06817","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-aware-fusing-and-tiling-of-neural","title":"MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference","date":"2021-07-14","arxiv_id":"2107.06960","repositories_listed":0,"syntology":null},{"url":null,"slug":"hant-hardware-aware-network-transformation","title":"LANA: Latency Aware Network Acceleration","date":"2021-07-12","arxiv_id":"2107.10624","repositories_listed":0,"syntology":null},{"url":null,"slug":"hemp-high-order-entropy-minimization-for","title":"HEMP: High-order Entropy Minimization for neural network comPression","date":"2021-07-12","arxiv_id":"2107.05298","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-embedded-iris-recognition-system","title":"An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes","date":"2021-07-08","arxiv_id":"2107.03688","repositories_listed":0,"syntology":null},{"url":null,"slug":"regional-differential-information-entropy-for","title":"Image restoration quality assessment based on regional differential information entropy","date":"2021-07-08","arxiv_id":"2107.03642","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-methods-for-joint-optimization","title":"Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks","date":"2021-07-06","arxiv_id":"2107.02520","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-valued-neural-communication","title":"Discrete-Valued Neural Communication","date":"2021-07-06","arxiv_id":"2107.02367","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modality-deep-restoration-of-extremely","title":"Multi-modality Deep Restoration of Extremely Compressed Face Videos","date":"2021-07-05","arxiv_id":"2107.05548","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-spinn-a-framework-for-quantizing-spiking","title":"Q-SpiNN: A Framework for Quantizing Spiking Neural Networks","date":"2021-07-05","arxiv_id":"2107.01807","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lottery-ticket-hypothesis-framework-for-low","title":"A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification","date":"2021-07-03","arxiv_id":"2107.01461","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-training-quantization-for-vision","title":"Post-Training Quantization for Vision Transformer","date":"2021-06-27","arxiv_id":"2106.14156","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-precision-training-in-logarithmic-number","title":"LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update","date":"2021-06-26","arxiv_id":"2106.13914","repositories_listed":0,"syntology":null},{"url":null,"slug":"countering-adversarial-examples-combining","title":"Countering Adversarial Examples: Combining Input Transformation and Noisy Training","date":"2021-06-25","arxiv_id":"2106.13394","repositories_listed":0,"syntology":null},{"url":null,"slug":"pqk-model-compression-via-pruning","title":"PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation","date":"2021-06-25","arxiv_id":"2106.14681","repositories_listed":0,"syntology":null},{"url":null,"slug":"preliminary-study-on-using-vector","title":"Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance","date":"2021-06-25","arxiv_id":"2106.13479","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-aware-training-ernie-and","title":"Quantization Aware Training, ERNIE and Kurtosis Regularizer: a short empirical study","date":"2021-06-24","arxiv_id":"2106.13035","repositories_listed":0,"syntology":null},{"url":null,"slug":"transform-based-feature-map-compression-for","title":"Transform-Based Feature Map Compression for CNN Inference","date":"2021-06-24","arxiv_id":"2106.12850","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-minimizing-symbol-error-rate-over-fading","title":"On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization","date":"2021-06-22","arxiv_id":"2106.11524","repositories_listed":0,"syntology":null},{"url":null,"slug":"over-the-air-computation-via-cloud-radio","title":"Over-the-Air Computation via Cloud Radio Access Networks","date":"2021-06-22","arxiv_id":"2106.11649","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-inference-via-universal-lsh-kernel","title":"Efficient Inference via Universal LSH Kernel","date":"2021-06-21","arxiv_id":"2106.11426","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-learning-based-precoder-codebooks-for","title":"Tensor Learning-based Precoder Codebooks for FD-MIMO Systems","date":"2021-06-21","arxiv_id":"2106.11374","repositories_listed":0,"syntology":null},{"url":null,"slug":"witten-type-topological-field-theory-of-self","title":"Witten-type topological field theory of self-organized criticality for stochastic neural networks","date":"2021-06-21","arxiv_id":"2106.10851","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-log-scale-quantization-for-low-cost","title":"Automated Log-Scale Quantization for Low-Cost Deep Neural Networks","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-sgd-via-gradient","title":"Communication Efficient SGD via Gradient Sampling With Bayes Prior","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-perceptual-preprocessing-for-video","title":"Deep Perceptual Preprocessing for Video Coding","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-aware-adaptive-multi-bit","title":"Distribution-Aware Adaptive Multi-Bit Quantization","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"event-based-bispectral-photometry-using","title":"Event-Based Bispectral Photometry Using Temporally Modulated Illumination","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-quantization-using-scaled-codebook","title":"Optimal Quantization Using Scaled Codebook","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pvgnet-a-bottom-up-one-stage-3d-object","title":"PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qpp-real-time-quantization-parameter","title":"QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-vlsi-circuit-constrains-on","title":"Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks","date":"2021-06-18","arxiv_id":"2106.10382","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-lies-optical-adversarial-attack","title":"Light Lies: Optical Adversarial Attack","date":"2021-06-18","arxiv_id":"2106.09908","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-effects-of-compression-with","title":"On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks","date":"2021-06-17","arxiv_id":"2106.09831","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-federated-learning-under","title":"Quantized Federated Learning under Transmission Delay and Outage Constraints","date":"2021-06-17","arxiv_id":"2106.09397","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-classification-of-cell-imaging","title":"Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map","date":"2021-06-17","arxiv_id":"2106.09444","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-white-paper-on-neural-network-quantization","title":"A White Paper on Neural Network Quantization","date":"2021-06-15","arxiv_id":"2106.08295","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-quantized-dnn-library-for","title":"Development of Quantized DNN Library for Exact Hardware Emulation","date":"2021-06-15","arxiv_id":"2106.08892","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-knowledge-distillation-for","title":"Energy-efficient Knowledge Distillation for Spiking Neural Networks","date":"2021-06-14","arxiv_id":"2106.07172","repositories_listed":0,"syntology":null},{"url":null,"slug":"fasticarl-fast-incremental-classifier-and","title":"FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications","date":"2021-06-14","arxiv_id":"2106.07268","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuroevolution-enhanced-multi-objective","title":"Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization","date":"2021-06-14","arxiv_id":"2106.07611","repositories_listed":0,"syntology":null},{"url":null,"slug":"dp-normfedavg-normalizing-client-updates-for","title":"On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates","date":"2021-06-13","arxiv_id":"2106.07094","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-enlightens-wireless-communication-analyses","title":"AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback","date":"2021-06-12","arxiv_id":"2106.06759","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-greedy-learning-of-cnns-for","title":"Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning","date":"2021-06-11","arxiv_id":"2106.06401","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-discrete-representation-learning","title":"Cross-Modal Discrete Representation Learning","date":"2021-06-10","arxiv_id":"2106.05438","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastening-the-initial-access-in-5g-nr","title":"Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks","date":"2021-06-10","arxiv_id":"2106.05716","repositories_listed":0,"syntology":null},{"url":null,"slug":"signalnet-a-low-resolution-sinusoid","title":"SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network","date":"2021-06-10","arxiv_id":"2106.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"verifying-quantized-neural-networks-using-smt","title":"Verifying Quantized Neural Networks using SMT-Based Model Checking","date":"2021-06-10","arxiv_id":"2106.05997","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-unrecognizable-faces-for-face","title":"Harnessing Unrecognizable Faces for Improving Face Recognition","date":"2021-06-08","arxiv_id":"2106.04112","repositories_listed":0,"syntology":null},{"url":null,"slug":"smoothness-aware-quantization-techniques","title":"Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques","date":"2021-06-07","arxiv_id":"2106.03524","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-unsupervised-learning-for-joint-antenna","title":"Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming","date":"2021-06-06","arxiv_id":"2106.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-dynamic-quantization-with-1","title":"Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution","date":"2021-06-04","arxiv_id":"2106.02295","repositories_listed":0,"syntology":null},{"url":null,"slug":"sigma-delta-and-distributed-noise-shaping","title":"Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features","date":"2021-06-04","arxiv_id":"2106.02614","repositories_listed":0,"syntology":null},{"url":null,"slug":"granger-causality-from-quantized-measurements","title":"Granger Causality from Quantized Measurements","date":"2021-06-03","arxiv_id":"2106.01513","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-distribution-sparsity-and-inference","title":"On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers","date":"2021-06-02","arxiv_id":"2106.01335","repositories_listed":0,"syntology":null},{"url":null,"slug":"passive-beamforming-design-for-intelligent","title":"Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems","date":"2021-06-02","arxiv_id":"2106.00890","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-resolution-encoder-decoder-networks-with","title":"Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation","date":"2021-06-01","arxiv_id":"2106.00566","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-bias-amplification-during-speed","title":"Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation","date":"2021-06-01","arxiv_id":"2106.00169","repositories_listed":0,"syntology":null},{"url":null,"slug":"weak-target-detection-with-multi-bit","title":"Weak target detection with multi-bit quantization in colocated MIMO radar","date":"2021-05-29","arxiv_id":"2106.00612","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-convergence-rate-for-a-distributed","title":"Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization","date":"2021-05-28","arxiv_id":"2105.14089","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-full-8-bit-integer-dnn","title":"Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization","date":"2021-05-27","arxiv_id":"2105.13890","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-probabilistic-pruning-a-general","title":"Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities","date":"2021-05-26","arxiv_id":"2105.12686","repositories_listed":0,"syntology":null},{"url":null,"slug":"dtnn-energy-efficient-inference-with-dendrite","title":"DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications","date":"2021-05-25","arxiv_id":"2105.11848","repositories_listed":0,"syntology":null}],"record_sha256":"8779f1987c1c770abd773ec024d301a84910c500336c4569026143684339ab5d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}