Is the Model Automatica Learning a Data Driven Architecture?

BI NMA 05:

NLP and Generative Models Panel

This is the 5th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 2nd of 3 in the deep learning series. In this episode, the panelists discuss their experiences “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).

Panelists

• Brad Wyble.
  • @bradpwyble.
• Kyunghyun Cho.
  • @kchonyc.
• He He.
  • @hhexiy.
• João Sedoc.
  • @JoaoSedoc.

The other panels:

• First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
• Second panel, about linear systems, real neurons, and dynamic networks.
• Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
• Fourth panel, about some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.