Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

melancholy

書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




書目名稱Neural Information Processing年度引用




書目名稱Neural Information Processing年度引用學(xué)科排名




書目名稱Neural Information Processing讀者反饋




書目名稱Neural Information Processing讀者反饋學(xué)科排名

violate

BANAL

condone

https://doi.org/10.1007/978-981-99-8070-3pattern recognition; affective and cognitive learning; big data; bioinformatics; brain-machine interface

青春期

SIT

價值在貶值

GRF-GMM: A Trajectory Optimization Framework for?Obstacle Avoidance in?Learning from?Demonstrationaussian mixture model/Gaussian mixture regression (GMM/GMR) has been widely used for its robustness and effectiveness. However, there still exist many problems of GMM when an obstacle, which is not presented in original demonstrations, appears in the workspace of robots. To address these problems, t

聲明

正常

CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with?Human-Environment-Roboonmental layout usually significantly impacts crowd distribution and robotic motion decision-making during crowded navigation. However, previous methods almost either learn and evaluate navigation strategies in unrealistic barrier-free settings or assume that expensive features like pedestrian speed

使人煩燥

Modeling User’s Neutral Feedback in?Conversational Recommendationns. Although CRS has shown success in generating recommendation lists based on user’s preferences, existing methods restrict users to make binary responses, i.e., accept and reject, after recommending, which limits users from expressing their needs. In fact, the user’s rejection feedback may contain