Lecture 4.1: Multimodal Representations (Multimodal Machine Learning, Carnegie Mellon University)
Lecture 4.1: Multimodal Representations (Multimodal Machine Learning, Carnegie Mellon University)
Topics: Graph Neural Networks. Multimodal auto-encoders. Multimodal joint representations.
----------------------------------------------------------------------------------------------------------------
Carnegie Mellon University 11-777 Multimodal Machine Learning, 2020 Fall
Website: https://cmu-multicomp-lab.github.io/mmml-course/fall2020/
Instructor: Louis-Philippe Morency
Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which studies computational approaches for modeling heterogenous data from multiple modalities. The course presents fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The course also discusses recent state-of-the-art models and applications of multimodal machine learning.
Видео Lecture 4.1: Multimodal Representations (Multimodal Machine Learning, Carnegie Mellon University) канала LP Morency
Topics: Graph Neural Networks. Multimodal auto-encoders. Multimodal joint representations.
----------------------------------------------------------------------------------------------------------------
Carnegie Mellon University 11-777 Multimodal Machine Learning, 2020 Fall
Website: https://cmu-multicomp-lab.github.io/mmml-course/fall2020/
Instructor: Louis-Philippe Morency
Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which studies computational approaches for modeling heterogenous data from multiple modalities. The course presents fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The course also discusses recent state-of-the-art models and applications of multimodal machine learning.
Видео Lecture 4.1: Multimodal Representations (Multimodal Machine Learning, Carnegie Mellon University) канала LP Morency
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Lecture 1.2: Datasets (Multimodal Machine Learning, Carnegie Mellon University)
Lecture 9.1: Reinforcement Learning (Multimodal Machine Learning, Carnegie Mellon University)
Lecture 7.1 - Multimodal Interaction (CMU Multimodal Machine Learning, Fall 2023)
Lecture 5.2: Alignment and Representation (Multimodal Machine Learning, Carnegie Mellon University)
Lecture 6.1 - Multimodal Transformers - Part2 (CMU Multimodal Machine Learning, Fall 2023)
Lecture 3.2 Language Representation, RNN (Multimodal Machine Learning, Carnegie Mellon University)
Lecture 12.1 - Multimodal Quantification (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 7.1 - Multimodal Reasoning - Part 2 (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 7.2 - Multimodal Inference and Knowledge (CMU Multimodal Machine Learning, Fall 2023)
Lecture 6.1 - Multimodal Aligned Representations (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 12.1 - New Research Direction (CMU Multimodal Machine Learning, Fall 2023)
Lecture 11.2 - Multimodal Transference (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 8.1: Discriminative Graphical Models (Multimodal Machine Learning, CMU)
Lecture 1.2 - Multimodal Research Tasks (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 4.1 - Multimodal Alignment (CMU Multimodal Machine Learning, Fall 2023)
Lecture 4.2 - Aligned Representation (CMU Multimodal Machine Learning, Fall 2023)
Lecture 3.2 - Multimodal Coordination and Fission (CMU Multimodal Machine Learning, Fall 2023)
Lecture 9.2 - Multimodal Generation - Part 2 (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 9.1 - Multimodal Generation (CMU Multimodal Machine Learning, Fall 2023)
Lecture 9.1 - Multimodal Generation - Part 1 (CMU Multimodal Machine Learning course, Fall 2022)
Lecture 12.2 - New Research Directions (CMU Multimodal Machine Learning course, Fall 2022)