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 1901–1950 of 4925 papers

TitleStatusHype
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks—0
Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Range—0
Towards Real-Time Neural Volumetric Rendering on Mobile Devices: A Measurement Study—0
HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization—0
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE—0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation MetricsCode0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
Q-SNNs: Quantized Spiking Neural Networks—0
High-Fidelity Facial Albedo Estimation via Texture Quantization—0
Attention-aware Post-training Quantization without Backpropagation—0
MSE Minimization in RIS-Aided MU-MIMO with Discrete Phase Shifts and Fronthaul Quantization—0
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization—0
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul—0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA—0
Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization—0
An Analysis on Quantizing Diffusion Transformers—0
Optimization of Armv9 architecture general large language model inference performance based on Llama.cppCode0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—0
Memory Faults in Activation-sparse Quantized Deep Neural Networks: Analysis and Mitigation using Sharpness-aware Training—0
GEB-1.3B: Open Lightweight Large Language Model—0
Optimizing Byte-level Representation for End-to-end ASR—0
One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model—0
Precipitation Nowcasting Using Physics Informed Discriminator Generative Models—0
Human-level molecular optimization driven by mol-gene evolution—0
MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction—0
ToneUnit: A Speech Discretization Approach for Tonal Language Speech Synthesis—0
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models—0
Q-S5: Towards Quantized State Space ModelsCode0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
Compressive Beam Alignment for Indoor Millimeter-Wave Systems—0
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases—0
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment—0
FoldToken2: Learning compact, invariant and generative protein structure language—0
T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text—0
TernaryLLM: Ternarized Large Language Model—0
Topological Analysis for Detecting Anomalies (TADA) in Time Series—0
Efficient Neural Compression with Inference-time Decoding—0
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks—0
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs—0
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization—0
Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs—0
Activation Map-based Vector Quantization for 360-degree Image Semantic Communication—0
Winner-takes-all learners are geometry-aware conditional density estimatorsCode0
Proofread: Fixes All Errors with One Tap—0
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model—0
Real-Time Spacecraft Pose Estimation Using Mixed-Precision Quantized Neural Network on COTS Reconfigurable MPSoCCode0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity—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