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 2451–2475 of 4925 papers

TitleStatusHype
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
Show:102550
← PrevPage 99 of 197Next →
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