https://github.com/mailong25/vietnamese-speech-recognition
Effectiveness of self-supervised pre-training for speech recognition
Papers with Code 아카이브
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/makinyilmaz/lhbdc
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/marccanby/bidi_decoding
Unsupervised Cross-lingual Representation Learning for Speech Recognition
https://github.com/marfoq/unsupervised_translator
https://github.com/mathworks/Baseline-MATLAB-DCASE
https://github.com/mauriceqch/pcc_geo_cnn_samples
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
https://github.com/may0324/DeepCompression-caffe
https://github.com/mbzuai-nlp/artst
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/mbzuai-nlp/sttatts
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/mdcnn/mdcnn.github.io
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
https://github.com/megvii-model/SinglePathOneShot
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/mengxiangming/dmps
Quantized Compressed Sensing with Score-based Generative Models
https://github.com/meteorshowers/StereoNet
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
https://github.com/microsoft/MInference
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
https://github.com/microsoft/ar2
https://github.com/microsoft/bitblas
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
https://github.com/microsoft/moonlit
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
https://github.com/mike9251/DAQ
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
https://github.com/milvus-io/milvus
https://github.com/mimbres/neural-audio-fp
https://github.com/mindspore-ai/models/tree/master/official/nlp/q8bert
https://github.com/mindspore-ai/models/tree/master/research/nlp/ternarybert
https://github.com/mingu600/compositional_code_learning
Compressing Word Embeddings via Deep Compositional Code Learning
https://github.com/mit-han-lab/ProxylessNAS
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
https://github.com/mit-han-lab/amc
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
https://github.com/mit-han-lab/lmquant
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-han-lab/mcunet
https://github.com/mit-han-lab/once-for-all
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
https://github.com/mkskeller/mnist-mpc
https://github.com/mlech26l/qnn_robustness_benchmarks
Scalable Verification of Quantized Neural Networks (Technical Report)
https://github.com/mobile-sensing-and-ubicomp-laboratory/normwear
https://github.com/modelscope/modelscope
https://github.com/mohammadimathstar/grlgq
https://github.com/mohdumar644/DoReFaNet-PyTorch
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
https://github.com/mr-eggplant/adaxpert0
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/mts-ai/replaceme
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.