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 1651–1700 of 4925 papers

TitleStatusHype
Continuous Control with Action Quantization from Demonstrations—0
Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?—0
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking—0
A Survey on Deep Hashing Methods—0
A Formalization of Image Vectorization by Region Merging—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
A Survey of Techniques for Optimizing Transformer Inference—0
A Survey of Small Language Models—0
A Flexible, Extensible Software Framework for Neural Net Compression—0
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search—0
L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization—0
1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture—0
Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry—0
Constraint Guided Model Quantization of Neural Networks—0
A Survey of Quantization Methods for Efficient Neural Network Inference—0
Constrained Approximate Similarity Search on Proximity Graph—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
A Survey of Methods for Low-Power Deep Learning and Computer Vision—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
Conditional Distribution Quantization in Machine Learning—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—0
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Computing with Hypervectors for Efficient Speaker Identification—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
How Does Batch Normalization Help Binary Training?—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition—0
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks—0
Accurate Block Quantization in LLMs with Outliers—0
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration—0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing—0
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization—0
A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Compress Polyphone Pronunciation Prediction Model with Shared Labels—0
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting—0
Compressive Spectrum Sensing with 1-bit ADCs—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Quantization for Fast Object Instance Search in Videos—0
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function—0
Associative Memories to Accelerate Approximate Nearest Neighbor Search—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