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Quantization
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Angle Estimation of a Single Source with Massive Uniform Circular Arrays
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Lightweight Federated Learning over Wireless Edge Networks
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation
Compress Any Segment Anything Model (SAM)
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation
EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
any4: Learned 4-bit Numeric Representation for LLMs
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
Analysis of Null Related Beampattern Measures and Signal Quantization Effects for Linear Differential Microphone Arrays
PsyLite Technical Report
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory Computing
Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links
CommVQ: Commutative Vector Quantization for KV Cache Compression
NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN
StainPIDR: A Pathological Image Decouplingand Reconstruction Method for Stain Normalization Based on Color Vector Quantization and Structure Restaining
TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models
From Tiny Machine Learning to Tiny Deep Learning: A Survey
A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.