Join The Alan Turing Institute for a talk with Nando de Freitas of Google DeepMind on training neural networks to solve many tasks with fewer data, followed by a Q&A and drinks reception.
Training a large neural network with lots of data and subsequently deploying this model to carry out specific tasks, such as speech recognition, machine translation, game playing, image recognition, image and text generation, text-to-speech, and lipreading has been incredibly fruitful. Instead of focusing on few tasks with massive amounts of data, this talk will however focus training neural networks to solve many tasks with few data each. The objective is not to learn a fixed-parameter classifier, but rather to learn a “prior” neural network that can be adapted rapidly to solve new tasks with few data. The output of training is not longer a fixed model, but rather a fast learner. That is, the goal is to build tools that learn.
Some technical knowledge required.
18:00-18:30 – Registration
18:30-18:35 – Welcome and introduction – Mark Briers (The Alan Turing Institute)
18:35-19:25 – Learning how to learn efficiently – Nando de Freitas (Google DeepMind)
19:25-19:40 – Q&A – Nando de Freitas and Mark Briers
19:40-20:30 – Drinks reception
Limited Spaces Remaining – Book here.