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
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
Compression Scaling Laws:Unifying Sparsity and Quantization—0
Compression strategies and space-conscious representations for deep neural networks—0
Compression without Quantization—0
Compressive Beam Alignment for Indoor Millimeter-Wave Systems—0
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function—0
Compressive Quantization for Fast Object Instance Search in Videos—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Spectrum Sensing with 1-bit ADCs—0
Compress Polyphone Pronunciation Prediction Model with Shared Labels—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization—0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
Computing with Hypervectors for Efficient Speaker Identification—0
Conditional Distribution Quantization in Machine Learning—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
Constrained Approximate Similarity Search on Proximity Graph—0
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
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Correlation Hashing Network for Efficient Cross-Modal Retrieval—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines—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