首页|Recent Findings from Carnegie Mellon University Provides New Insights into Machi ne Learning (Foundations and Trends In Multimodal Machine Learning: Principles, Challenges, and Open Questions)
Recent Findings from Carnegie Mellon University Provides New Insights into Machi ne Learning (Foundations and Trends In Multimodal Machine Learning: Principles, Challenges, and Open Questions)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Investigators publish new report on Ma chine Learning. According to news originating from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, "Multimodal machine learning is a vibran t multi-disciplinary research field that aims to design computer agents with int elligent capabilities such as understanding, reasoning, and learning through int egrating multiple communicative modalities, including linguistic, acoustic, visu al, tactile, and physiological messages. With the recent interest in video under standing, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machin e learning has brought unique computational and theoretical challenges to the ma chine learning community given the heterogeneity of data sources and the interco nnections often found between modalities."
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningCa rnegie Mellon University
NETL
NSTL
