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 2401–2450 of 4925 papers

TitleStatusHype
A White Paper on Neural Network Quantization—0
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition—0
Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry—0
Just CHOP: Embarrassingly Simple LLM Compression—0
Large Language Models For Text Classification: Case Study And Comprehensive Review—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation—0
LAST: Language Model Aware Speech Tokenization—0
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels—0
Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals—0
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks—0
Lattice Functions for the Analysis of Analog-to-Digital Conversion—0
Lattice Quantization—0
Lattice Representation Learning—0
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement—0
Deep neural networks are robust to weight binarization and other non-linear distortions—0
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models—0
Layer-wise Quantization for Quantized Optimistic Dual Averaging—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
LCQ: Low-Rank Codebook based Quantization for Large Language Models—0
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding—0
Light Lies: Optical Adversarial Attack—0
LeanTTA: A Backpropagation-Free and Stateless Approach to Quantized Test-Time Adaptation on Edge Devices—0
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search—0
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs—0
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications—0
Learned layered coding for Successive Refinement in the Wyner-Ziv Problem—0
Deep Neural Network Models Compression—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training—0
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision—0
Learning A Deep _ Encoder for Hashing—0
Learning A Disentangling Representation For PU Learning—0
Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence—0
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT—0
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates—0
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image—0
An Adaptive Statistical Non-uniform Quantizer for Detail Wavelet Components in Lossy JPEG2000 Image Compression—0
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization—0
Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
LG-VQ: Language-Guided Codebook Learning—0
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks—0
A Vision System for Multi-View Face Recognition—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