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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–675 of 4925 papers

TitleStatusHype
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank ResidualsCode1
A Survey on Inference Optimization Techniques for Mixture of Experts ModelsCode3
Self-control: A Better Conditional Mechanism for Masked Autoregressive Model—0
Autoregressive Video Generation without Vector QuantizationCode4
On the Compression of Language Models for Code: An Empirical Study on CodeBERT—0
More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression—0
VidTok: A Versatile and Open-Source Video TokenizerCode3
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting—0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation—0
QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models—0
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation—0
Fast and Slow Gradient Approximation for Binary Neural Network OptimizationCode0
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion ModelsCode1
VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression—0
Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models—0
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes—0
Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its DeploymentCode0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks—0
TinySubNets: An efficient and low capacity continual learning strategyCode0
Memory-Efficient 4-bit Preconditioned Stochastic Optimization—0
Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array—0
Progressive Compression with Universally Quantized Diffusion Models—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