Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking

1 code implementation • 20 Nov 2019 • Eric Crawford, Joelle Pineau

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process.

Object Object Tracking

Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning

no code implementations • 5 Jul 2019 • Srinivas Venkattaramanujam, Eric Crawford, Thang Doan, Doina Precup

Goal-conditioned policies are used in order to break down complex reinforcement learning (RL) problems by using subgoals, which can be defined either in state space or in a latent feature space.

reinforcement-learning Reinforcement Learning (RL) +1

BanditSum: Extractive Summarization as a Contextual Bandit

1 code implementation • EMNLP 2018 • Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung

In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.

Extractive Summarization Extractive Text Summarization

Sequential Coordination of Deep Models for Learning Visual Arithmetic

no code implementations • ICLR 2018 • Eric Crawford, Guillaume Rabusseau, Joelle Pineau

Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive.