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 1951–2000 of 4925 papers

TitleStatusHype
Bit Efficient Quantization for Deep Neural Networks—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling—0
Generative Design of Hardware-aware DNNs—0
Generative Diffusion Models for Lattice Field Theory—0
Highly Efficient and Effective LLMs with Multi-Boolean Architectures—0
Generative QoE Modeling: A Lightweight Approach for Telecom Networks—0
Generative Semantic Communication for Text-to-Speech Synthesis—0
High-Perceptual Quality JPEG Decoding via Posterior Sampling—0
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation—0
Convergence Rates for Regularized Optimal Transport via Quantization—0
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models—0
A Biresolution Spectral Framework for Product Quantization—0
Geometry and clustering with metrics derived from separable Bregman divergences—0
Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-training—0
Getting Free Bits Back from Rotational Symmetries in LLMs—0
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation—0
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images—0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes—0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning—0
Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification—0
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees—0
Global synchronization of multi-agent systems with nonlinear interactions—0
Goal-oriented compression for L_p-norm-type goal functions: Application to power consumption scheduling—0
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation—0
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM—0
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference—0
Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements—0
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters—0
gpcgc: a green point cloud geometry coding method—0
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
GPTQT: Quantize Large Language Models Twice to Push the Efficiency—0
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
GPTVQ: The Blessing of Dimensionality for LLM Quantization—0
Correlated quantization for distributed mean estimation and optimization—0
GQ-Net: Training Quantization-Friendly Deep Networks—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks—0
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training—0
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization—0
Gradient Based Method for the Fusion of Lattice Quantizers—0
Gradient-Based Post-Training Quantization: Challenging the Status Quo—0
Gradient Descent Quantizes ReLU Network Features—0
Does Video Compression Impact Tracking Accuracy?—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