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 251–275 of 4925 papers

TitleStatusHype
StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space—0
NoWag: A Unified Framework for Shape Preserving Compression of Large Language ModelsCode1
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference—0
Lightweight Road Environment Segmentation using Vector Quantization—0
Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs—0
The Binary and Ternary Quantization Can Improve Feature Discrimination—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
Chinese-Vicuna: A Chinese Instruction-following Llama-based ModelCode7
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code SelectionCode1
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning—0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Résumé abstractif à partir d'une transcription audio—0
ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization—0
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM—0
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
CSPLADE: Learned Sparse Retrieval with Causal Language Models—0
Neural Network Emulation of the Classical Limit in Quantum Systems via Learned Observable Mappings—0
Simultaneous Input and State Estimation under Output Quantization: A Gaussian Mixture approach—0
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
Asymptotic stabilization under homomorphic encryption: A re-encryption free method—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