This book provides an introduction to the theory of manifolds in an easy readable way. Key concepts of manifolds, angent spaces and Lie group / Lie algebra are presented. Furthermore, it gives an introduction into differential forms, integration and De Rham theory.
Archives: Resources
Resource description
From Images to Text New forms of Human-AI Interaction
Recent progress in the Computer Vision and Natural Language Processing communities have made it possible to connect Vision and Language together in a variety of different tasks which lie at the intersection of Vision, Language, and Embodied AI. Those tasks range from generating meaningful descriptions of images, to answering questions and navigating agents in unseen… Continue reading From Images to Text New forms of Human-AI Interaction
Video Summarization Using Deep Neural Networks: A Survey
Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on… Continue reading Video Summarization Using Deep Neural Networks: A Survey
Machine Listening for Music and Sound Analysis
Lecture material on Machine Listening for Music and Sound Analysis lecture
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that generate toxic, inaccurate, and unhelpful content, and automatic evaluation metrics often fail to identify these behaviors. As models become more capable, human feedback is an invaluable signal for evaluating… Continue reading Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Representation Learning for Natural Language Processing
Provides a comprehensive overview of the representation learning techniques for natural language processing. Presents a systematic and thorough introduction to the theory, algorithms and applications of representation learning. Shares insights into the future research directions for each topic as well as for the overall field of representation learning for natural language processing.