Deep Learning Workshop @ICML'16

Reddit Ask-Workshop-Anything!

To make the workshop even more engaging and wide-reaching, we're gathering comments and questions on those two topics via Reddit "Ask a Workshop Anything". We will collect and pass them on to the invited speakers so that they are ready and happy to address some of those comments. We will share the answers by the invited speakers as a form of online video recording of the workshop.

Here are the links: 

- Session 1:

- Session 2:


Deep learning is a fast-growing field of machine learning concerned with the study and design of computer algorithms for learning good representations of data, at multiple levels of abstraction. There has been rapid progress in this area in recent years, both in terms of methods and in terms of applications, which are attracting the major IT companies as well as major research labs. Many challenges remain, however, in aspects like large sample complexity of deep learning approaches, generative modelling, learning representations for reinforcement learning and symbolic reasoning, modelling of temporal data with long-term dependencies, efficient Bayesian inference for deep learning and multi-modal data and models. This workshop aims at tackling two major challenges in deep learning, which are unsupervised learning in the regime of small data, and simulation-based learning and its transferability to real world, by bringing together researchers in the field of deep learning.

Date and Location

  • Date: 23 June 2016
  • Location: Marriott Marquis: Westside Ballroom 3 & 4

Aim and Expected Outcome

The aim of this workshop is to spread important new ideas in the field and promote communication and collaboration between members of the community. The bulk of the workshop will be split into two sessions, each consisting of a set of invited talks followed by a panel discussion. By organizing the workshop in this manner we aim to promote focused discussions that dive deep into important areas and also increase interaction between speakers and the audience. This year's questions are:

"What is deep learning in the small data regime?" which will be accompanied with a sub-question "Does unsupervised learning have a central role in this? What else is essential when dealing with low sample complexity?" 

"What does simulation-based learning bring to the table?" accompanied with the sub-questions "How transferrable is the knowledge learned from a simulation to the real world?", "Are simulated environments the way to achieve machine intelligence?" and "How important is it for agents to simulate the world in their minds?"

Confirmed Invited speakers

Session 1: Deep Learning in the Small Data Regime
  • Harri Valpola, Curious AI Company
  • Leon Bottou, Facebook AI
  • Joelle Pineau, McGill University
  • Brenden Lake, NYU Center for Data Science
  • Anima Anandkumar, UC Irvine
Session 2: Simulation-based Learning
  • Raia Hadsell, Google DeepMind
  • Pieter Abbeel, UC Berkeley & OpenAI
  • Gary Marcus, NYU & Geometric Intelligence
  • Marco Baroni, University of Trento
  • Sanja Fidler, University of Toronto


  • Antoine Bordes, Facebook
  • Kyunghyun Cho, NYU
  • Emily Denton, NYU
  • Rob Fergus, Facebook & NYU
  • Nando de Freitas, Google DeepMind & Oxford