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Found 4 papers, 2 papers with code
Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking
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.
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning
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.
BanditSum: Extractive Summarization as a Contextual Bandit
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.
Ranked #11 on
Extractive Text Summarization
on CNN / Daily Mail
Sequential Coordination of Deep Models for Learning Visual Arithmetic
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.
