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 3851–3900 of 4925 papers

TitleStatusHype
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Brain-inspired reverse adversarial examples—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs—0
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines—0
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation—0
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)—0
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization—0
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
BRICS: Bi-level feature Representation of Image CollectionS—0
BTEL: A Binary Tree Encoding Approach for Visual Localization—0
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers—0
Bullion: A Column Store for Machine Learning—0
Byzantine-Resilient Secure Federated Learning—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
CacheQuant: Comprehensively Accelerated Diffusion Models—0
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
CAMBI: Contrast-aware Multiscale Banding Index—0
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data—0
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?—0
Can Large Language Models Understand Context?—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
CBQ: Cross-Block Quantization for Large Language Models—0
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning—0
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization—0
CEG4N: Counter-Example Guided Neural Network Quantization Refinement—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
Cell growth rate dictates the onset of glass to fluid-like transition and long time super-diffusion in an evolving cell colony—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference—0
Channel-Aware Constellation Design for Digital OTA Computation—0
Channel Balancing for Accurate Quantization of Winograd Convolutions—0
Channel Estimation for MIMO Hybrid Architectures with Low Resolution ADCs for mmWave Communication—0
Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCs—0
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method—0
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
Channel-Wise Mixed-Precision Quantization for Large Language Models—0
Characterising Bias in Compressed Models—0
Characterization of the frequency response of channel-interleaved photonic ADCs based on the optical time-division demultiplexer—0
Characterizing Coherent Integrated Photonic Neural Networks under Imperfections—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models—0
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge—0
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models—0
CHIME: A Compressive Framework for Holistic Interest Modeling—0
Show:102550
← PrevPage 78 of 99Next →
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