https://github.com/pinto0309/onnx2tf
A Quantization-Friendly Separable Convolution for MobileNets
Papers with Code 아카이브
Quantization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pipiPdesu/random_quantize
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
https://github.com/pkorus/l3ic
https://github.com/pminhtam/xnor_conv_pytorch_extension
https://github.com/polimi-ispl/larsnet
https://github.com/pommedeterresautee/fastrtext
https://github.com/ppke-nlpg/fastText_factored-cbow
https://github.com/pra-dan/Worth-Reading-ML-Papers
https://github.com/princeton-pli/prulong
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads
https://github.com/puallee/Online-dictionary-learning
ECG beats classification via online sparse dictionary and time pyramid matching
https://github.com/pulp-platform/multispectral-riemannian
https://github.com/pulp-platform/nemo
Technical Report: NEMO DNN Quantization for Deployment Model
https://github.com/pwc-1/Paper-10/tree/main/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2_conformer
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/pwc-1/Paper-9/tree/main/1/xlm_roberta_xl
Unsupervised Cross-lingual Representation Learning for Speech Recognition
https://github.com/pwc-1/Paper-9/tree/main/ibert
https://github.com/pytorch/ao
https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/qinyao-he/bit-rnn
Effective Quantization Methods for Recurrent Neural Networks
https://github.com/qualcomm-ai-research/outlier-free-transformers
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
https://github.com/quic/aimet-model-zoo
https://github.com/qwenlm/qwen
https://github.com/qwenlm/qwen-vl
https://github.com/qwenlm/qwen1.5
https://github.com/qwenlm/qwen2-vl
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/qwopqwop200/GPTQ-for-LLaMa
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/racinmat/depth-voxelmap-estimation
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference
https://github.com/raja-kumar/folding-batchnorm
Data-Free Quantization Through Weight Equalization and Bias Correction
https://github.com/raminrasoulinezhad/DeepBenchVerilog
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
https://github.com/ramsesproject/TomoGAN
https://github.com/raotnameh/AML_Course/tree/main/project
Generalized Product Quantization Network for Semi-supervised Image Retrieval
https://github.com/rasmith/vllm
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/revsic/torch-retriever-vc
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
https://github.com/ricky40403/DSQ
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
https://github.com/ricky40403/GDFQ
https://github.com/rmenegaux/fastDNA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.