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 1551–1575 of 4925 papers

TitleStatusHype
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis—0
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization—0
Optimizing Large Language Models through Quantization: A Comparative Analysis of PTQ and QAT Techniques—0
Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection FrameworkCode0
An asymmetric heuristic for trained ternary quantization based on the statistics of the weights: an application to medical signal classificationCode0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization—0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
Rate-aware Compression for NeRF-based Volumetric Video—0
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
Compressive Spectrum Sensing with 1-bit ADCs—0
Green My LLM: Studying the key factors affecting the energy consumption of code assistants—0
Saliency Assisted Quantization for Neural Networks—0
Multi-bit Distributed Detection of Sparse Stochastic Signals over Error-Prone Reporting Channels—0
An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal ImagingCode0
Interactions Across Blocks in Post-Training Quantization of Large Language Models—0
Sum Rate Maximization in the Constant Envelope MIMO Downlink with the RZF Precoder—0
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety AlignmentCode0
Hybrid Beamforming for Integrated Sensing and Communications With Low Resolution DACs—0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization—0
BF-IMNA: A Bit Fluid In-Memory Neural Architecture for Neural Network Acceleration—0
Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density EstimationCode0
Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective—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