Lasse Espeholt | DeepAI

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∙ 09/01/2021

Boosting Search Engines with Interactive Agents

Can machines learn to use a search engine as an interactive tool for fin...

83 Leonard Adolphs, et al. ∙

∙ 04/19/2021

Agent-Centric Representations for Multi-Agent Reinforcement Learning

Object-centric representations have recently enabled significant progres...

0 Wenling Shang, et al. ∙

∙ 03/24/2020

MetNet: A Neural Weather Model for Precipitation Forecasting

Weather forecasting is a long standing scientific challenge with direct ...

0 Casper Kaae Sønderby, et al. ∙

∙ 10/15/2019

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

We present a modern scalable reinforcement learning agent called SEED (S...

0 Lasse Espeholt, et al. ∙

∙ 07/25/2019

Google Research Football: A Novel Reinforcement Learning Environment

Recent progress in the field of reinforcement learning has been accelera...

16 Karol Kurach, et al. ∙

∙ 09/12/2018

Multi-task Deep Reinforcement Learning with PopArt

The reinforcement learning community has made great strides in designing...

0 Matteo Hessel, et al. ∙

∙ 02/05/2018

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

In this work we aim to solve a large collection of tasks using a single ...

0 Lasse Espeholt, et al. ∙

∙ 10/31/2016

Neural Machine Translation in Linear Time

We present a novel neural network for processing sequences. The ByteNet ...

0 Nal Kalchbrenner, et al. ∙

∙ 06/16/2016

Conditional Image Generation with PixelCNN Decoders

This work explores conditional image generation with a new image density...

0 Aaron van den Oord, et al. ∙

∙ 06/10/2015

Teaching Machines to Read and Comprehend

Teaching machines to read natural language documents remains an elusive ...

0 Karl Moritz Hermann, et al. ∙

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