Papers with Code 아카이브
Quantization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 보존된 평가 기록
- 32
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hyn2028/llm-cxr
Vision-Language Generative Model for View-Specific Chest X-ray Generation
https://github.com/iabd/QuantizedNMT
https://github.com/idsia-robotics/pulp-frontnet
Technical Report: NEMO DNN Quantization for Deployment Model
https://github.com/indix/whatthelang
https://github.com/intel/ipex-llm
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
https://github.com/intel/neural-speed
https://github.com/internlm/xtuner
https://github.com/iriskaplan/latticequant
https://github.com/isadrtdinov/wavenet
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder
https://github.com/isha-garg/Deep_Compression
https://github.com/ist-daslab/panzamail
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
https://github.com/ito-rafael/once-for-all-2
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch
https://github.com/jafermarq/WinogradAwareNets
https://github.com/jakc4103/DFQ
Data-Free Quantization Through Weight Equalization and Bias Correction
https://github.com/jakc4103/piecewise-quantization
Post-Training Piecewise Linear Quantization for Deep Neural Networks
https://github.com/jakc4103/scale-adjusted-training
https://github.com/jameszampa/ECE-570-Implementation
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
https://github.com/jameszampa/VIP-SoCET-Benchmark
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
https://github.com/jayzhang0727/stochastic-path-following-quantization
Post-training Quantization for Neural Networks with Provable Guarantees
https://github.com/jbellis/jvector
Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
https://github.com/jen1995/fastText
https://github.com/jeremyiv/diffc
https://github.com/jerrywu-code/susgen
https://github.com/jiali-ms/JLM
https://github.com/jiaweizzhao/galore
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
https://github.com/jinghuichen/fedcams
https://github.com/jingtaozhan/DRhard
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
https://github.com/jingtaozhan/LearningToRetrieve
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
https://github.com/joetho786/Respiratory-Sound-Classification-in-Wearable-Devices-Enabled-by-Patient-Specific-Model-Tuning
https://github.com/jordiclive/Convert-PolyAI-Torch
ConveRT: Efficient and Accurate Conversational Representations from Transformers
https://github.com/js1010/cuhnsw
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
https://github.com/jun-fang/PWLQ
Post-Training Piecewise Linear Quantization for Deep Neural Networks
https://github.com/junjya/faiss
https://github.com/kachayev/ssl-in-one-epoch
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.