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

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Showing 2351–2400 of 4925 papers

TitleStatusHype
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models—0
Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications—0
The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks—0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments—0
Resource Constrained Semantic Segmentation for Waste SortingCode0
Exploring Post-Training Quantization of Protein Language ModelsCode0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
QWID: Quantized Weed Identification Deep neural networkCode0
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise—0
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation—0
NIF: A Fast Implicit Image Compression with Bottleneck Layers and Modulated Sinusoidal ActivationsCode0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
General Point Model with Autoencoding and Autoregressive—0
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte CarloCode0
Transmitting Data Through Reconfigurable Intelligent Surface: A Spatial Sigma-Delta Modulation Approach—0
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs—0
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
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
Functional Invariants to Watermark Large Transformers—0
TEQ: Trainable Equivalent Transformation for Quantization of LLMs—0
Image Compression using only Attention based Neural Networks—0
Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion—0
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language ModelsCode0
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations—0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification—0
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines—0
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources—0
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks—0
Distillation Improves Visual Place Recognition for Low Quality ImagesCode0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers—0
Vector Quantized Multi-modal Guidance for Alzheimer’s Disease Diagnosis Based on Feature ImputationCode0
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM—0
Sub-token ViT Embedding via Stochastic Resonance TransformersCode0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
VaSAB: The variable size adaptive information bottleneck for disentanglement on speech and singing voice—0
Learning A Disentangling Representation For PU Learning—0
Soft Convex Quantization: Revisiting Vector Quantization with Convex 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