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 2601–2625 of 4925 papers

TitleStatusHype
Constraint Guided Model Quantization of Neural Networks—0
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis—0
Continuous Control with Action Quantization from Demonstrations—0
Continuous Speech Synthesis using per-token Latent Diffusion—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
Convergence of Federated Learning over a Noisy Downlink—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
Convergence rate of Tsallis entropic regularized optimal transport—0
Convergence Rates for Regularized Optimal Transport via Quantization—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
Convex Quantization Preserves Logconcavity—0
Convolutional neural network compression for natural language processing—0
Convolutional Neural Network Quantization using Generalized Gamma Distribution—0
Convolutional Neural Networks Quantization with Attention—0
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters—0
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness—0
Coreset-Based Neural Network Compression—0
Correlated quantization for distributed mean estimation and 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