Ari Morcos

Research Scientist

I am a research scientist at Facebook AI Research working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, I’ve worked on understanding the properties predictive of generalization, methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, I worked at DeepMind in London, and I earned my PhD in neurobiology at Harvard University, using machine learning to study the cortical dynamics underlying evidence accumulation for decision-making.


Understanding deep learning, generalization, abstract reasoning, computer vision, representation learning

Latest Publications

Learning for Dynamics & Control (L4DC) - June 10, 2020

Plan2vec: Unsupervised Representation Learning by Latent Plans

Ge Yang, Amy Zhang, Ari Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra

ICLR - April 27, 2020

The Early Phase of Neural Network Training

Jonathan Frankle, David J. Schwab, Ari Morcos

ICLR - April 26, 2020

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra

NeurIPS - December 10, 2019

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

ICLR - May 10, 2019

Insights on Visual Representations for Embodied Navigation Tasks

Erik Wijmans, Julian Straub, Dhruv Batra, Judy Hoffman, Ari Morcos