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

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Showing 2901–2950 of 4925 papers

TitleStatusHype
ReTAG: Reasoning Aware Table to Analytic Text Generation—0
Stochastic Gradient Langevin Dynamics Based on Quantization with Increasing Resolution—0
Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems—0
Stochastic Learning Equation using Monotone Increasing Resolution of Quantization—0
Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees—0
Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization—0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM—0
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Streaming Parrotron for on-device speech-to-speech conversion—0
Streamlining Tensor and Network Pruning in PyTorch—0
Strong Solutions and Quantization-Based Numerical Schemes for a Class of Non-Markovian Volatility Models—0
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation—0
Structural Latency Perturbation in Large Language Models Through Recursive State Induction—0
Structured adaptive and random spinners for fast machine learning computations—0
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation—0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
Neural Language of Thought Models—0
Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection—0
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification—0
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation—0
Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals—0
Style Quantization for Data-Efficient GAN Training—0
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition—0
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach—0
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets—0
Subgraph Stationary Hardware-Software Inference Co-Design—0
SUBIC: A supervised, structured binary code for image search—0
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display—0
Sublinear quantum algorithms for training linear and kernel-based classifiers—0
Subspace Robust Wasserstein Distances—0
Subtensor Quantization for Mobilenets—0
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime—0
Sum Rate Maximization in the Constant Envelope MIMO Downlink with the RZF Precoder—0
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization—0
Super-relaxation of space-time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation—0
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images—0
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning—0
Supervised Learning in the Presence of Concept Drift: A modelling framework—0
Supervised Matrix Factorization for Cross-Modality Hashing—0
Supervised Quantization for Similarity Search—0
Support Recovery in Universal One-bit Compressed Sensing—0
Survey of Quantization Techniques for On-Device Vision-based Crack Detection—0
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency—0
SUT System Description for Anti-Spoofing 2017 Challenge—0
SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention—0
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers—0
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration—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