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 4551–4575 of 4925 papers

TitleStatusHype
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference—0
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning—0
Fast Template Evaluation with Vector Quantization—0
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs—0
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
FATNN: Fast and Accurate Ternary Neural Networks—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
FBI: Fingerprinting models with Benign Inputs—0
FBQuant: FeedBack Quantization for Large Language Models—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
FD Cell-Free mMIMO: Analysis and Optimization—0
FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?—0
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression—0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data—0
Feature Quantization for Defending Against Distortion of Images—0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Federated Learning in Adversarial Settings—0
Federated Learning: Strategies for Improving Communication Efficiency—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—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