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Found 13 papers, 4 papers with code
Learning to encode spatial relations from natural language
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
We propose an artificial life framework aimed at facilitating the emergence of intelligent organisms.
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
Shaping Belief States with Generative Environment Models for RL
We propose a way to efficiently train expressive generative models in complex environments.
TF-Replicator: Distributed Machine Learning for Researchers
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow.
Learning models for visual 3D localization with implicit mapping
We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels.
Encoding Spatial Relations from Natural Language
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fail to capture certain kinds of information implicit in the real world.
Temporal Difference Variational Auto-Encoder
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.
Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models.
Convolution by Evolution: Differentiable Pattern Producing Networks
In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN.
Towards Conceptual Compression
We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling.
Ranked #64 on
Image Generation
on CIFAR-10
(bits/dimension metric)
