SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

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Showing 2351–2400 of 4925 papers

TitleStatusHype
A comprehensive review of Binary Neural Network—0
Interest Point Detection based on Adaptive Ternary Coding—0
Joint Neural Architecture Search and Quantization—0
Joint Optimization of Rate, Distortion, and Decoding Energy for HEVC Intraframe Coding—0
Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference—0
Bang for the Buck: Vector Search on Cloud CPUs—0
Interactions Across Blocks in Post-Training Quantization of Large Language Models—0
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers—0
Reconfigurable Intelligent Surface-induced Randomness for mmWave Key Generation—0
Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints—0
A White Paper on Neural Network Quantization—0
Joint Texture and Geometry Optimization for RGB-D Reconstruction—0
Joshua 4.0: Packing, PRO, and Paraphrases—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
JPEG-LM: LLMs as Image Generators with Canonical Codec Representations—0
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Learning low-precision neural networks without Straight-Through Estimator(STE)—0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Deep neural networks are robust to weight binarization and other non-linear distortions—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
Kernel k-Medoids as General Vector Quantization—0
Kernel Quantization for Efficient Network Compression—0
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models—0
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning—0
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression—0
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes—0
Knowledge Distillation: A Survey—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
Learning Linear Block Codes with Gradient Quantization—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer—0
KurTail : Kurtosis-based LLM Quantization—0
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs—0
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization—0
KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache—0
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications—0
Deep Neural Network Models Compression—0
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization—0
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
Design of Stochastic Quantizers for Privacy Preservation—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Learning Sparse Low-Precision Neural Networks With Learnable Regularization—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified