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 1701–1725 of 4925 papers

TitleStatusHype
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation—0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness—0
Reduced bit median quantization: A middle process for Efficient Image Compression—0
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models—0
TalkMosaic: Interactive PhotoMosaic with Multi-modal LLM Q&A Interactions—0
NDVQ: Robust Neural Audio Codec with Normal Distribution-Based Vector Quantization—0
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models—0
Scaling FP8 training to trillion-token LLMs—0
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview—0
Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference—0
Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)—0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
Forearm Ultrasound based Gesture Recognition on Edge—0
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation—0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
Investigating Disentanglement in a Phoneme-level Speech Codec for Prosody Modeling—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
Dequantization of a signal from two parallel quantized observations—0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM—0
NVRC: Neural Video Representation Compression—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—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