Representation Learning @ Scale
Abstract
Machine learning techniques are reaching or exceeding human level performances in tasks involving simple data like image classification, translation, and text-to-speech. The success of these machine learning algorithms is attributed to highly versatile representations learnt from data using deep networks or intricately designed Bayesian models. Representation learning has also provided hints in neuroscience, e.g. understanding how humans might categorize objects. Despite these instances of success, progress has been limited to simple data-types so far.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2018
- Accession Number
- AD1167995
Entities
People
- Manzil Zaheer
Organizations
- Carnegie Mellon University