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 4001–4050 of 4925 papers

TitleStatusHype
Quantization Algorithms for Random Fourier Features—0
Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding—0
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging—0
Quantization Aware Factorization for Deep Neural Network Compression—0
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control—0
Quantization-Aware Phase Retrieval—0
Quantization Aware Training, ERNIE and Kurtosis Regularizer: a short empirical study—0
Quantization Backdoors to Deep Learning Commercial Frameworks—0
Quantization-based Bermudan option pricing in the FX world—0
Quantization-based Bounds on the Wasserstein Metric—0
Quantization based Fast Inner Product Search—0
Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization—0
Quantization-based Optimization with Perspective of Quantum Mechanics—0
Quantization Design for Deep Learning-Based CSI Feedback—0
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization—0
Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training—0
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization—0
Quantization for decentralized learning under subspace constraints—0
Quantization for Rapid Deployment of Deep Neural Networks—0
Quantization goes Polynomial—0
Quantization-Guided Training for Compact TinyML Models—0
Quantization in Layer's Input is Matter—0
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation—0
Quantization Loss Re-Learning Method—0
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning—0
Quantization Mimic: Towards Very Tiny CNN for Object Detection—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
Quantization of Deep Neural Networks for Accumulator-constrained Processors—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Quantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware—0
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation—0
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study—0
Quantization of Large Language Models with an Overdetermined Basis—0
Quantization optimized with respect to the Haar basis—0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices—0
Quantization Under the Real-world Measure: Fast and Accurate Valuation of Long-dated Contracts—0
Quantized Adam with Error Feedback—0
Quantized Adaptive Subgradient Algorithms and Their Applications—0
Quantized and Asynchronous Federated Learning—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Quantized Approximately Orthogonal Recurrent Neural Networks—0
Quantized Compressive K-Means—0
Quantized Consensus under Data-Rate Constraints and DoS Attacks: A Zooming-In and Holding Approach—0
Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm—0
Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction—0
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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