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 4576–4600 of 4925 papers

TitleStatusHype
Federated Learning With Quantized Global Model Updates—0
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization—0
Federated Split BERT for Heterogeneous Text Classification—0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
FedHQ: Hybrid Runtime Quantization for Federated Learning—0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization—0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning—0
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification—0
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment—0
FewGAN: Generating from the Joint Distribution of a Few Images—0
FFN Fusion: Rethinking Sequential Computation in Large Language Models—0
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference—0
Fighting over-fitting with quantization for learning deep neural networks on noisy labels—0
Fighting Quantization Bias With Bias—0
Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks—0
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs—0
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs—0
Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation—0
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing—0
Finite-Bit Quantization For Distributed Algorithms With Linear Convergence—0
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization—0
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation—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