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Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we address the problem of image-goal navigation in the con...
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Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Learning to navigate in a realistic setting where an agent must rely sol...
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Accessing Higher-level Representations in Sequential Transformers with Feedback Memory
Transformers are feedforward networks that can process input tokens in p...
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Augmenting Self-attention with Persistent Memory
Transformer networks have lead to important progress in language modelin...
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Adaptive Attention Span in Transformers
We propose a novel self-attention mechanism that can learn its optimal a...
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Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Learning when to communicate and doing that effectively is essential in ...
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Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
In hierarchical reinforcement learning a major challenge is determining ...
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Planning with Arithmetic and Geometric Attributes
A desirable property of an intelligent agent is its ability to understan...
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Composable Planning with Attributes
The tasks that an agent will need to solve often are not known during tr...
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Simple Baseline for Visual Question Answering
We describe a very simple bag-of-words baseline for visual question answ...
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MazeBase: A Sandbox for Learning from Games
This paper introduces MazeBase: an environment for simple 2D games, desi...
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End-To-End Memory Networks
We introduce a neural network with a recurrent attention model over a po...
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Training Convolutional Networks with Noisy Labels
The availability of large labeled datasets has allowed Convolutional Net...
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Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences
Learning invariant representations from images is one of the hardest cha...
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