sqlダイレクトリ間のクラウド移行における翻訳

confidence-self-calibration-for-multi-label-class-incremental-learning-study

ゼロショット薬物間相互作用を予測するための記述学習

raman分光法によるマイクロゲルサイズの非線形多様体学習

irstd-diff-generative-approach-for-infrared-small-target-detection

不均衡svm分类的自适应加权软边框的广义框架

時間区間の端点における拡散モデルの特異性に取り組む

raf-gi-robust-accurate-and-fast-convergent-gradient-inversion-attack-in-federated-learning


The core message of this paper is to learn safe output feedback control laws for unknown systems with uncertain dynamics and state estimation errors by constructing robust output control barrier functions (ROCBFs) from expert driving demonstrations.


coremsg

learning-safe-control-laws-from-expert-driving-demonstrations-with-uncertain-dynamics-and-state-estimation


Learning Safe Control Laws from Expert Driving Demonstrations with Uncertain Dynamics and State Estimation


title_rewrite

Autonomous Driving Control