This CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification....
This CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification....
He attends a 4-year university in Dallas, Texas, and is majoring in Mechanical Engineering taking his Introduction to the Fundamentals of Science course. Each aspect of the learning process as it relates to multimodal instruction in 2023 is outlined through the experiences of Juan to situate the...
The difficulty of how to apply deep learning methods to the diagnosis of BPPV lies in how to combine eye movement information (video)with head position information (vector). Accordingly, we propose a BPPV diagnosis model based on a multimodal deep learning approach. In this study, we ...
Deep learning is a subfield of AI that employs a type of algorithm called an artificial neural network to address complex tasks. The current generative AI revolution is powered by deep learning models, particularly transformers, which are a type of neural architecture. ...
Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive ...
Deep Multimodal Representation Learning: A Survey; Wenzhong Guo et al The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges; Maria Lymperaiou et al Augmented Language Models: a Survey; Grégoire Mialon et al Multimodal Deep Learning; Matthias Aßenmacher ...
Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches, it is essential to understand how uncertainty impacts these models. We propose LUMA, a unique benchmark dataset, featuring...
6. 文献来源: Course video recommendation with multimodal information in online learning platforms: A deep learning framework一篇值得借鉴不多的文章 在这个框架中,不同种类的课程信息,如课程标题、课程音频和课程评论,被用来在在线学习平台上进行适当的推荐。此外,我们利用显性和隐性反馈来推断学习者的偏好。基于真...
This CMU Multimodal Machine Learning course presents the fundamental mathematical concepts in machine learning and deep learning relevant to the six main challenges in multimodal research: (1) representation, (2) alignment, (3) reasoning, (4) generation, (5) transference and (6) quantification....