{"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/30","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":30,"pages_in_order":50,"rows_per_page":100,"rows":[2901,3000],"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/29","next":"/task/quantization/papers/31","papers":[{"url":null,"slug":"norm-tweaking-high-performance-low-bit","title":"Norm Tweaking: High-performance Low-bit Quantization of Large Language Models","date":"2023-09-06","arxiv_id":"2309.02784","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-efficient-vision-transformers","title":"A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking","date":"2023-09-05","arxiv_id":"2309.02031","repositories_listed":0,"syntology":null},{"url":null,"slug":"ohq-on-chip-hardware-aware-quantization","title":"On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks","date":"2023-09-05","arxiv_id":"2309.01945","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantease-optimization-based-quantization-for","title":"QuantEase: Optimization-based Quantization for Language Models","date":"2023-09-05","arxiv_id":"2309.01885","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustedge-low-power-adversarial-detection","title":"RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems","date":"2023-09-05","arxiv_id":"2310.06845","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fly-deep-neural-network-optimization","title":"On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision","date":"2023-09-04","arxiv_id":"2309.01824","repositories_listed":0,"syntology":null},{"url":null,"slug":"softmax-bias-correction-for-quantized","title":"Softmax Bias Correction for Quantized Generative Models","date":"2023-09-04","arxiv_id":"2309.01729","repositories_listed":0,"syntology":null},{"url":null,"slug":"edkm-an-efficient-and-accurate-train-time","title":"eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models","date":"2023-09-02","arxiv_id":"2309.00964","repositories_listed":0,"syntology":null},{"url":null,"slug":"fptq-fine-grained-post-training-quantization","title":"FPTQ: Fine-grained Post-Training Quantization for Large Language Models","date":"2023-08-30","arxiv_id":"2308.15987","repositories_listed":0,"syntology":null},{"url":null,"slug":"implementation-and-evaluation-of-physical","title":"Implementation and Evaluation of Physical Layer Key Generation on SDR based LoRa Platform","date":"2023-08-30","arxiv_id":"2308.15696","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-learning-with-binary-neural","title":"On-Device Learning with Binary Neural Networks","date":"2023-08-29","arxiv_id":"2308.15308","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-vq-compression-for-tractable-internet","title":"MEMORY-VQ: Compression for Tractable Internet-Scale Memory","date":"2023-08-28","arxiv_id":"2308.14903","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-learned-lossless-jpeg-recompression","title":"Efficient Learned Lossless JPEG Recompression","date":"2023-08-25","arxiv_id":"2308.13287","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-noise-shaping-for-audio-coding-using","title":"Hybrid noise shaping for audio coding using perfectly overlapped window","date":"2023-08-24","arxiv_id":"2308.12566","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-distributed-nash-equilibrium","title":"Quantized distributed Nash equilibrium seeking under DoS attacks","date":"2023-08-24","arxiv_id":"2308.12617","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-models-decompress-race-biases-what","title":"Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition","date":"2023-08-23","arxiv_id":"2308.11840","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-delay-consistent-signal-reconstruction","title":"Consistent Signal Reconstruction from Streaming Multivariate Time Series","date":"2023-08-23","arxiv_id":"2308.12459","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-energy-resource-management-all","title":"Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond","date":"2023-08-22","arxiv_id":"2308.11263","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-clip-free-quantized-super-resolution","title":"Towards Clip-Free Quantized Super-Resolution Networks: How to Tame Representative Images","date":"2023-08-22","arxiv_id":"2308.11365","repositories_listed":0,"syntology":null},{"url":null,"slug":"qd-bev-quantization-aware-view-guided","title":"QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection","date":"2023-08-21","arxiv_id":"2308.10515","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-from-autoencoders-latent-space-via","title":"Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts","date":"2023-08-21","arxiv_id":"2308.10704","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-based-optimization-with","title":"Quantization-based Optimization with Perspective of Quantum Mechanics","date":"2023-08-20","arxiv_id":"2308.11594","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-quantization-in-tvm","title":"Analyzing Quantization in TVM","date":"2023-08-19","arxiv_id":"2308.10905","repositories_listed":0,"syntology":null},{"url":null,"slug":"funquant-a-r-package-to-perform-quantization","title":"FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations","date":"2023-08-18","arxiv_id":"2308.10871","repositories_listed":0,"syntology":null},{"url":null,"slug":"resq-residual-quantization-for-video","title":"ResQ: Residual Quantization for Video Perception","date":"2023-08-18","arxiv_id":"2308.09511","repositories_listed":0,"syntology":null},{"url":null,"slug":"shark-a-lightweight-model-compression","title":"SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems","date":"2023-08-18","arxiv_id":"2308.09395","repositories_listed":0,"syntology":null},{"url":null,"slug":"jpeg-quantized-coefficient-recovery-via-dct","title":"JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer","date":"2023-08-17","arxiv_id":"2308.09110","repositories_listed":0,"syntology":null},{"url":null,"slug":"finequant-unlocking-efficiency-with-fine","title":"FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs","date":"2023-08-16","arxiv_id":"2308.09723","repositories_listed":0,"syntology":null},{"url":null,"slug":"precision-and-recall-reject-curves-for","title":"Precision and Recall Reject Curves for Classification","date":"2023-08-16","arxiv_id":"2308.08381","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-model-compression-for-large","title":"A Survey on Model Compression for Large Language Models","date":"2023-08-15","arxiv_id":"2308.07633","repositories_listed":0,"syntology":null},{"url":null,"slug":"akvsr-audio-knowledge-empowered-visual-speech","title":"AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model","date":"2023-08-15","arxiv_id":"2308.07593","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-based-post-training-quantization","title":"Gradient-Based Post-Training Quantization: Challenging the Status Quo","date":"2023-08-15","arxiv_id":"2308.07662","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-neural-pde-solvers-using","title":"Efficient Neural PDE-Solvers using Quantization Aware Training","date":"2023-08-14","arxiv_id":"2308.07350","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-data-free-compression-pruning-and","title":"Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning","date":"2023-08-14","arxiv_id":"2308.07209","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-aware-mixed-precision","title":"Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation","date":"2023-08-12","arxiv_id":"2308.06422","repositories_listed":0,"syntology":null},{"url":null,"slug":"nupes-non-uniform-post-training-quantization","title":"NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search","date":"2023-08-10","arxiv_id":"2308.05600","repositories_listed":0,"syntology":null},{"url":null,"slug":"relu-and-addition-based-gated-rnn","title":"ReLU and Addition-based Gated RNN","date":"2023-08-10","arxiv_id":"2308.05629","repositories_listed":0,"syntology":null},{"url":null,"slug":"fpga-resource-aware-structured-pruning-for","title":"FPGA Resource-aware Structured Pruning for Real-Time Neural Networks","date":"2023-08-09","arxiv_id":"2308.05170","repositories_listed":0,"syntology":null},{"url":null,"slug":"safer-layer-level-sensitivity-assessment-for","title":"SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference","date":"2023-08-09","arxiv_id":"2308.04753","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-aware-factorization-for-deep","title":"Quantization Aware Factorization for Deep Neural Network Compression","date":"2023-08-08","arxiv_id":"2308.04595","repositories_listed":0,"syntology":null},{"url":null,"slug":"fliqs-one-shot-mixed-precision-floating-point","title":"FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search","date":"2023-08-07","arxiv_id":"2308.03290","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantize-then-estimate-protocol-for-csi","title":"Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach","date":"2023-08-04","arxiv_id":"2308.02316","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-disentangled-features-in-neural","title":"Frequency Disentangled Features in Neural Image Compression","date":"2023-08-04","arxiv_id":"2308.02620","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustmq-benchmarking-robustness-of-quantized","title":"RobustMQ: Benchmarking Robustness of Quantized Models","date":"2023-08-04","arxiv_id":"2308.02350","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-analysis-of-cordic-processor-with-fpga","title":"Error Analysis of CORDIC Processor with FPGA Implementation","date":"2023-08-02","arxiv_id":"2308.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"tango-rethinking-quantization-for-graph","title":"Tango: rethinking quantization for graph neural network training on GPUs","date":"2023-08-02","arxiv_id":"2308.00890","repositories_listed":0,"syntology":null},{"url":null,"slug":"aquila-communication-efficient-federated-1","title":"AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy","date":"2023-08-01","arxiv_id":"2308.00258","repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-federated-learning-with-1","title":"Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation","date":"2023-08-01","arxiv_id":"2308.00263","repositories_listed":0,"syntology":null},{"url":null,"slug":"mrq-support-multiple-quantization-schemes","title":"MRQ:Support Multiple Quantization Schemes through Model Re-Quantization","date":"2023-08-01","arxiv_id":"2308.01867","repositories_listed":0,"syntology":null},{"url":null,"slug":"alternate-learning-based-sparse-semantic","title":"Alternate Learning based Sparse Semantic Communications for Visual Transmission","date":"2023-07-31","arxiv_id":"2309.16681","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automata-theoretic-approach-to","title":"An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks","date":"2023-07-29","arxiv_id":"2307.15907","repositories_listed":0,"syntology":null},{"url":null,"slug":"metts-multilingual-emotional-text-to-speech","title":"METTS: Multilingual Emotional Text-to-Speech by Cross-speaker and Cross-lingual Emotion Transfer","date":"2023-07-29","arxiv_id":"2307.15951","repositories_listed":0,"syntology":null},{"url":null,"slug":"incrementally-computable-neural-networks","title":"Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs","date":"2023-07-27","arxiv_id":"2307.14988","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-volumetric-reconstruction-for","title":"High-Resolution Volumetric Reconstruction for Clothed Humans","date":"2023-07-25","arxiv_id":"2307.13282","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-compression-methods-for-yolov5-a-review","title":"Model Compression Methods for YOLOv5: A Review","date":"2023-07-21","arxiv_id":"2307.11904","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-federated-learning-21","title":"Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks","date":"2023-07-20","arxiv_id":"2307.10815","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-split-learning-via","title":"Communication-Efficient Split Learning via Adaptive Feature-Wise Compression","date":"2023-07-20","arxiv_id":"2307.10805","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-feature-distillation-for-network","title":"Quantized Feature Distillation for Network Quantization","date":"2023-07-20","arxiv_id":"2307.10638","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-conditional-slot-attention-for","title":"Grounded Object Centric Learning","date":"2023-07-18","arxiv_id":"2307.09437","repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-image-compression-using-fine-tuned","title":"Extreme Image Compression using Fine-tuned VQGANs","date":"2023-07-17","arxiv_id":"2307.08265","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-bit-rate-binaural-link-for-improved-ultra","title":"Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids","date":"2023-07-17","arxiv_id":"2307.08858","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-techniques-for-optimizing","title":"A Survey of Techniques for Optimizing Transformer Inference","date":"2023-07-16","arxiv_id":"2307.07982","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-kernel-modulated-neural","title":"Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression","date":"2023-07-12","arxiv_id":"2307.06143","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-distilled-quantization-achieving-high","title":"Self-Distilled Quantization: Achieving High Compression Rates in Transformer-Based Language Models","date":"2023-07-12","arxiv_id":"2307.05972","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-error-of-first-order-methods","title":"Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles","date":"2023-07-10","arxiv_id":"2307.04679","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-yolop-quantization-aware-you-only-look-once","title":"Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception","date":"2023-07-10","arxiv_id":"2307.04537","repositories_listed":0,"syntology":null},{"url":null,"slug":"qbitopt-fast-and-accurate-bitwidth","title":"QBitOpt: Fast and Accurate Bitwidth Reallocation during Training","date":"2023-07-10","arxiv_id":"2307.04535","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-information-loss-for-spiking-neural","title":"InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks","date":"2023-07-10","arxiv_id":"2307.04356","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-in-memory-computing","title":"Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives","date":"2023-07-08","arxiv_id":"2307.03936","repositories_listed":0,"syntology":null},{"url":null,"slug":"ita-an-energy-efficient-attention-and-softmax","title":"ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers","date":"2023-07-07","arxiv_id":"2307.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"free-bits-latency-optimization-of-mixed","title":"Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge","date":"2023-07-06","arxiv_id":"2307.02894","repositories_listed":0,"syntology":null},{"url":null,"slug":"greedy-selection-for-heterogeneous-sensors","title":"Greedy Selection for Heterogeneous Sensors","date":"2023-07-03","arxiv_id":"2307.00840","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-free-quantization-via-mixed-precision","title":"Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning","date":"2023-07-02","arxiv_id":"2307.00498","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-nomp-for-line-spectrum-estimation","title":"Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm","date":"2023-07-02","arxiv_id":"2307.00491","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-the-influence-of-final-resolution","title":"Analysis of the influence of final resolution on ADC accuracy","date":"2023-07-01","arxiv_id":"2307.00388","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-a-relation-between-the-rate-distortion","title":"On a Relation Between the Rate-Distortion Function and Optimal Transport","date":"2023-07-01","arxiv_id":"2307.00246","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-yolo-efficient-inference-for-real-time","title":"Q-YOLO: Efficient Inference for Real-time Object Detection","date":"2023-07-01","arxiv_id":"2307.04816","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-oversampling-in-uplink-massive","title":"Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs","date":"2023-06-30","arxiv_id":"2306.17697","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-strong-baselines-for-ternary-neural","title":"Designing strong baselines for ternary neural network quantization through support and mass equalization","date":"2023-06-30","arxiv_id":"2306.17442","repositories_listed":0,"syntology":null},{"url":null,"slug":"relu-neural-networks-polyhedral","title":"ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog","date":"2023-06-30","arxiv_id":"2306.17418","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlimited-sampling-radar-a-real-time-end-to","title":"Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator","date":"2023-06-30","arxiv_id":"2306.17684","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structurally-regularized-cnn-architecture","title":"A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition","date":"2023-06-29","arxiv_id":"2306.16604","repositories_listed":0,"syntology":null},{"url":null,"slug":"dna-teq-an-adaptive-exponential-quantization","title":"DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference","date":"2023-06-28","arxiv_id":"2306.16430","repositories_listed":0,"syntology":null},{"url":null,"slug":"inr-mdsqc-implicit-neural-representation","title":"INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding","date":"2023-06-24","arxiv_id":"2306.13919","repositories_listed":0,"syntology":null},{"url":null,"slug":"partitioning-guided-k-means-extreme-empty","title":"Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression","date":"2023-06-24","arxiv_id":"2306.14031","repositories_listed":0,"syntology":null},{"url":null,"slug":"qnnrepair-quantized-neural-network-repair","title":"QNNRepair: Quantized Neural Network Repair","date":"2023-06-23","arxiv_id":"2306.13793","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-storage-on-synthetic-dna-using-1","title":"Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders","date":"2023-06-22","arxiv_id":"2306.12882","repositories_listed":0,"syntology":null},{"url":null,"slug":"subgraph-stationary-hardware-software","title":"Subgraph Stationary Hardware-Software Inference Co-Design","date":"2023-06-21","arxiv_id":"2306.17266","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynaquant-compressing-deep-learning-training","title":"DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization","date":"2023-06-20","arxiv_id":"2306.11800","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-complexity-multidimensional-dct","title":"Low-complexity Multidimensional DCT Approximations","date":"2023-06-20","arxiv_id":"2306.11724","repositories_listed":0,"syntology":null},{"url":null,"slug":"pushing-the-limits-of-3d-shape-generation-at","title":"Pushing the Limits of 3D Shape Generation at Scale","date":"2023-06-20","arxiv_id":"2306.11510","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-cell-modeling-of-li-ion-polymer","title":"Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles","date":"2023-06-19","arxiv_id":"2306.10654","repositories_listed":0,"syntology":null},{"url":null,"slug":"magnificent-minified-models","title":"Magnificent Minified Models","date":"2023-06-16","arxiv_id":"2306.10177","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-compression-using-binarization","title":"Neural Network Compression using Binarization and Few Full-Precision Weights","date":"2023-06-15","arxiv_id":"2306.08960","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-training-trajectories-of-deep-single","title":"High-performance deep spiking neural networks with 0.3 spikes per neuron","date":"2023-06-14","arxiv_id":"2306.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-quantized-graph-auto-encoder","title":"Discrete Graph Auto-Encoder","date":"2023-06-13","arxiv_id":"2306.07735","repositories_listed":0,"syntology":null},{"url":null,"slug":"mfas-emotion-recognition-through-multiple","title":"MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition","date":"2023-06-12","arxiv_id":"2306.09361","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-neural-networks-using","title":"Resource Efficient Neural Networks Using Hessian Based Pruning","date":"2023-06-12","arxiv_id":"2306.07030","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-inductive-generative-adversarial","title":"Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search","date":"2023-06-12","arxiv_id":"2306.06928","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-neural-network-compression-via","title":"End-to-End Neural Network Compression via $\\frac{\\ell_1}{\\ell_2}$ Regularized Latency Surrogates","date":"2023-06-09","arxiv_id":"2306.05785","repositories_listed":0,"syntology":null}],"record_sha256":"d5ad8e9657388e47dc281e8c018916761a8fbf384bc5e0f2802a25e78f0f2cd7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}