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 2101–2150 of 4925 papers

TitleStatusHype
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative ModelsCode0
SignSGD with Federated VotingCode0
Work in Progress: Linear Transformers for TinyML—0
On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model PerformanceCode0
Neural Image Compression with Quantization Rectifier—0
GLAD: Improving Latent Graph Generative Modeling with Simple QuantizationCode0
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective—0
Magic for the Age of Quantized DNNs—0
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization—0
NoisyDECOLLE: Robust Local Learning for SNNs on Neuromorphic HardwareCode0
Provable Privacy with Non-Private Pre-Processing—0
Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural NetworksCode0
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space—0
Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance—0
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression—0
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?Code0
CRB Analysis for Mixed-ADC Based DOA Estimation—0
Generalized Relevance Learning Grassmann QuantizationCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems—0
Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding—0
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization—0
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation—0
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning—0
Enhancing Multimodal Unified Representations for Cross Modal Generalization—0
Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision—0
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers—0
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN InferenceCode0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks—0
Adaptive Integrate-and-Fire Time Encoding Machine with Quantization—0
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs—0
Design of Stochastic Quantizers for Privacy Preservation—0
VQSynery: Robust Drug Synergy Prediction With Vector Quantization Mechanism—0
Deep-Learned Compression for Radio-Frequency Signal Classification—0
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization—0
Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000Code0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Better Schedules for Low Precision Training of Deep Neural Networks—0
A Hierarchical Federated Learning Approach for the Internet of Things—0
On the Compressibility of Quantized Large Language Models—0
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD DetectionCode0
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-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