Simon Kornblith | Understanding neural networks from a representational perspective: effects of width and depth


  • Datum: 19.07.2021
  • Uhrzeit: 16:00 - 17:00
  • Vortragende(r): Simon Kornblith
  • Research scientist, Google, Mountain View, California, USA
  • Ort: MPI für Kognitions- und Neurowissenschaften
  • Raum: Zoom Meeting
  • Gastgeber: CBS CoCoNUT

Deep learning involves complex interactions between data, architecture, and training objectives. In this talk, I will present work toward understanding these interactions through the lens of neural network representations. I will first introduce the challenges of measuring similarity between neural network representations. I will then discuss the use of centered kernel alignment (CKA) as a similarity index between representations, and show that it addresses these challenges. Finally, I will describe our recent work applying CKA and other tools to understand how neural network representations change as networks become wider or deeper.


Meeting ID: 961 0650 8785

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