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 1876–1900 of 4925 papers

TitleStatusHype
Codebook Features: Sparse and Discrete Interpretability for Neural NetworksCode1
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach—0
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs—0
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte CarloCode0
General Point Model with Autoencoding and Autoregressive—0
Structured Multi-Track Accompaniment Arrangement via Style Prior ModellingCode1
LLM-FP4: 4-Bit Floating-Point Quantized TransformersCode2
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph RepresentationCode0
LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding—0
Federated learning compression designed for lightweight communicationsCode0
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations—0
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI—0
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization—0
An Overview on IEEE 802.11bf: WLAN Sensing—0
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models—0
VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge—0
Matrix Compression via Randomized Low Rank and Low Precision FactorizationCode1
Watermarking LLMs with Weight QuantizationCode1
Functional Invariants to Watermark Large Transformers—0
BitNet: Scaling 1-bit Transformers for Large Language ModelsCode2
TEQ: Trainable Equivalent Transformation for Quantization of LLMs—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