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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opengvlab/videomamba
공식 코드VideoMamba: State Space Model for Efficient Video Understanding
https://github.com/openmedlab/pathoduet
공식 코드PathoTune: Adapting Visual Foundation Model to Pathological Specialists
https://github.com/openseg-group/openseg.pytorch
공식 코드https://github.com/openvinotoolkit/nncf
공식 코드Neural Network Compression Framework for fast model inference
https://github.com/openvinotoolkit/nncf_pytorch
공식 코드Neural Network Compression Framework for fast model inference
https://github.com/optimization-ai/drrho-clip
공식 코드Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
https://github.com/optimization-for-data-driven-science/dp-with-public-data
공식 코드Optimal Differentially Private Model Training with Public Data
https://github.com/optml-group/bip
공식 코드https://github.com/optml-group/unlearn-sparse
공식 코드https://github.com/orrzohar/lovm
공식 코드https://github.com/oscarkey/interlocking-backprop
공식 코드Interlocking Backpropagation: Improving depthwise model-parallelism
https://github.com/ososos888/prune-then-distill
공식 코드https://github.com/osu-nlp-group/webdreamer
공식 코드Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
https://github.com/otroshi/edgeface
공식 코드https://github.com/owensgroup/ml_perf_model
공식 코드Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
https://github.com/pagand/armcmc
공식 코드Online Probabilistic Model Identification using Adaptive Recursive MCMC
https://github.com/pairlab/vagram
공식 코드https://github.com/panmt/mbrl_with_isolated_imaginations
공식 코드Model-Based Reinforcement Learning with Isolated Imaginations
https://github.com/paperswithcode/galai
공식 코드https://github.com/parisafarmanifard/iris-sam
공식 코드https://github.com/pasqualeturin/lamp
공식 코드https://github.com/paulgavrikov/cnn-filter-db
공식 코드An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
https://github.com/pauljaffe/vam
공식 코드https://github.com/pbelcak/neregrain
공식 코드https://github.com/pbihao/slm
공식 코드https://github.com/pds-dpo/pds-dpo
공식 코드Multimodal Preference Data Synthetic Alignment with Reward Model
https://github.com/pengr/contrastive_autoeval
공식 코드https://github.com/pengr/energy_autoeval
공식 코드https://github.com/peteanderson80/Matterport3DSimulator
공식 코드https://github.com/philvanlane/chronoflow
공식 코드https://github.com/phm-bgu/public_dynamic_model_for_gear_vibrations
공식 코드https://github.com/pjsac/transworldng
공식 코드https://github.com/pkmtum/d3c-uq
공식 코드A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
https://github.com/pku-dair/air
공식 코드https://github.com/placebovitamin/mpc-tutorial
공식 코드Implementation of Linear Model Predictive Control -- Tutorial
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.