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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tylerachang/multilingual-geometry
공식 코드https://github.com/tylerkastner/distribution-equivalence
공식 코드Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
https://github.com/tyuxie/artree
공식 코드ARTree: A Deep Autoregressive Model for Phylogenetic Inference
https://github.com/uark-aicv/sam3d
공식 코드https://github.com/uber-research/FSDM
공식 코드https://github.com/ubiquitouslearning/mobilefm
공식 코드https://github.com/ucas-vg/sambor
공식 코드Boosting Segment Anything Model Towards Open-Vocabulary Learning
https://github.com/ucl-pplv/learnlib
공식 코드https://github.com/uclaml/sppo
공식 코드Self-Play Preference Optimization for Language Model Alignment
https://github.com/ucsc-real/model_transferability
공식 코드https://github.com/udellgroup/oboe
공식 코드https://github.com/ufal/kazitext
공식 코드Understanding Model Robustness to User-generated Noisy Texts
https://github.com/uiuc-focal-lab/comet
공식 코드https://github.com/ukplab/iclr2024-model-merging
공식 코드https://github.com/ukplab/iclr2025-psa
공식 코드Differentially Private Steering for Large Language Model Alignment
https://github.com/ulab-uiuc/gwm
공식 코드https://github.com/ulab-uiuc/iranker
공식 코드https://github.com/uncbiag/unigradicon
공식 코드uniGradICON: A Foundation Model for Medical Image Registration
https://github.com/univ-esuty/ambifusion
공식 코드https://github.com/unkux/Learn_CF
공식 코드Predicting Cascading Failures with a Hyperparametric Diffusion Model
https://github.com/unstable-zeros/dl-mpc-sls
공식 코드Data-driven Distributed and Localized Model Predictive Control
https://github.com/urchade/hner
공식 코드Hierarchical Transformer Model for Scientific Named Entity Recognition
https://github.com/uservan/speculative_thinking
공식 코드Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
https://github.com/utahIRlab/AMTurk-Product-Search-Explanation-Evaluation
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/utahIRlab/drem-attention
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/utkuozbulak/regional-adversarial-perturbation
공식 코드https://github.com/uu-sml/calibration
공식 코드https://github.com/uwFengyuan/OCC-CLIP
공식 코드Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution
https://github.com/uzh-rpg/high_mpc
공식 코드https://github.com/vahuynh/prs
공식 코드https://github.com/vaishnavmohit/MAREO
공식 코드https://github.com/valarzz/dlpa
공식 코드Model-based Reinforcement Learning for Parameterized Action Spaces
https://github.com/valarzz/model-based-reinforcement-learning-for-parameterized-action-spaces
공식 코드Model-based Reinforcement Learning for Parameterized Action Spaces
https://github.com/valentyn1boreiko/llm-threat-model
공식 코드A Realistic Threat Model for Large Language Model Jailbreaks
https://github.com/valeoai/ConfidNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.