Transfer Learning, Covariant Learning and Parallel Transport

Explore transfer learning concepts in this 50-minute lecture by Antoine Cornuejols from Institut des Hautes Etudes Scientifiques (IHES). Delve into the importance of transfer learning in reducing training costs for new models. Examine the standard neural network approach of reusing learned representations and adapting decision functions. Discover a dual algorithm that adapts representations while maintaining decision functions, and learn about early classification of time series. Question the notion of bias in transfer learning, consider information costs, and explore necessary a priori assumptions for transfer learning guarantees. Recognize how reasoning by analogy and online learning relate to transfer learning. Investigate the application of parallel transport and covariant physics concepts to transfer learning challenges. Gain insights from Antoine Cornuejols of MIA/AgroParisTech on advancing transfer learning techniques and their practical implications.