https://github.com/lunglmayrmoser/alexsnn
공식 코드Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Bounds
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/luomingshuang/m3gpt
공식 코드M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
https://github.com/luoyingsong/daq
공식 코드DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
https://github.com/luv91/vqgan_project
공식 코드https://github.com/lxa9867/imagefolder
공식 코드ImageFolder: Autoregressive Image Generation with Folded Tokens
https://github.com/lxa9867/qsd
공식 코드https://github.com/lxxxxr/cacom
공식 코드Context-aware Communication for Multi-agent Reinforcement Learning
https://github.com/lynshao/NoisyNN
공식 코드Denoising Noisy Neural Networks: A Bayesian Approach with Compensation
https://github.com/lyxx3rd/legaleval-q
공식 코드LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text
https://github.com/lzhangbv/acpsgd
공식 코드Evaluation and Optimization of Gradient Compression for Distributed Deep Learning
https://github.com/lzj994/Binary-Quantization
공식 코드An Integrated Approach to Produce Robust Models with High Efficiency
https://github.com/m-wojnar/zdc
공식 코드Applying generative neural networks for fast simulations of the ALICE (CERN) experiment
https://github.com/mac-automl/ompq
공식 코드https://github.com/macaronlin/llama3-quantization
공식 코드An empirical study of LLaMA3 quantization: from LLMs to MLLMs
https://github.com/maciejzieba/HyperFlow
공식 코드https://github.com/magic-research/dataset_quantization
공식 코드https://github.com/magics-lab/outeffhop
공식 코드Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
https://github.com/maifoundations/qzo
공식 코드Fine-tuning Quantized Neural Networks with Zeroth-order Optimization
https://github.com/maincold2/Compact-3DGS
공식 코드https://github.com/maincold2/FFNeRV
공식 코드FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
https://github.com/malekijoo/FedZipRevision
공식 코드FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
https://github.com/mali-farooq/thermal-yolo-and-model-optimization-using-tensorflowlite
공식 코드https://github.com/mandt-lab/bayesian-ac
공식 코드https://github.com/mandt-lab/vbq
공식 코드https://github.com/mangelroman/audio2score
공식 코드A holistic approach to polyphonic music transcription with neural networks
https://github.com/mariusarvinte/deep-llr-quantization
공식 코드https://github.com/mariuskrumm/manybodymeps
공식 코드Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
https://github.com/marsjacobs/kd-qat-large-enc
공식 코드https://github.com/marsjacobs/ti-kd-qat
공식 코드https://github.com/marsupialtail/sparsednn
공식 코드https://github.com/martaandronic/neuralut
공식 코드NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable Functions
https://github.com/martin-wey/peft-llm-code
공식 코드Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
https://github.com/mashaan14/ASC-noisy
공식 코드Approximate spectral clustering density-based similarity for noisy datasets
https://github.com/mashaan14/RPTree
공식 코드The Effect of Points Dispersion on the $k$-nn Search in Random Projection Forests
https://github.com/mashaan14/Spectral-Clustering
공식 코드Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
https://github.com/mauriceqch/pcc_geo_cnn
공식 코드Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.