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 951–1000 of 4925 papers

TitleStatusHype
Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation—0
Towards AI-Native Fronthaul: Neural Compression for NextG Cloud RAN—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical ModelCode0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
TaDA: Training-free recipe for Decoding with Adaptive KV Cache Compression and Mean-centering—0
FPTQuant: Function-Preserving Transforms for LLM Quantization—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
Massive MIMO with 1-Bit DACs: Data Detection for Quantized Linear Precoding with Dithering—0
PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling—0
Kernel k-Medoids as General Vector Quantization—0
Nonlinear Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation with Hardware Impairments—0
BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing—0
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector QuantizationCode0
Quantized Dissipative Uncertain Model for Fractional T_S Fuzzy systems with Time_Varying Delays Under Networked Control System—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compression—0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
Flexible Mixed Precision Quantization for Learned Image CompressionCode0
Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs—0
Structured Pruning and Quantization for Learned Image CompressionCode0
Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025—0
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer—0
Quantization-based Bounds on the Wasserstein Metric—0
Power-of-Two (PoT) Weights in Large Language Models (LLMs)—0
LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal TextCode0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach—0
LittleBit: Ultra Low-Bit Quantization via Latent Factorization—0
MuLoCo: Muon is a practical inner optimizer for DiLoCo—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Merge-Friendly Post-Training Quantization for Multi-Target Domain AdaptationCode0
Revisiting Uncertainty Estimation and Calibration of Large Language Models—0
Highly Efficient and Effective LLMs with Multi-Boolean Architectures—0
Climate Finance BenchCode0
On the Interplay of Privacy, Persuasion and Quantization—0
Does quantization affect models' performance on long-context tasks?Code0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—0
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
Optimizing edge AI models on HPC systems with the edge in the loopCode0
Efficient Speech Translation through Model Compression and Knowledge DistillationCode0
Communication-Efficient Multi-Device Inference Acceleration for Transformer ModelsCode0
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency GuaranteesCode0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingCode0
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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