Results for "Demis Hassabis"

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Approximate Hubel-Wiesel Modules and the Data Structures of Neural ComputationDec 28 2015This paper describes a framework for modeling the interface between perception and memory on the algorithmic level of analysis. It is consistent with phenomena associated with many different brain regions. These include view-dependence (and invariance) ... More
Model-Free Episodic ControlJun 14 2016State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance. Humans, on the other hand, can very quickly exploit highly rewarding nuances of an environment upon first discovery. In the brain, ... More
Learning and Querying Fast Generative Models for Reinforcement LearningFeb 08 2018A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called ... More
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmDec 05 2017The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that ... More
Grounded Language Learning in a Simulated 3D WorldJun 20 2017Jun 26 2017We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language ... More
Overcoming catastrophic forgetting in neural networksDec 02 2016The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist ... More
Imagination-Augmented Agents for Deep Reinforcement LearningJul 19 2017Feb 14 2018We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe ... More
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsJan 24 2018Feb 04 2018Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial ... More
The Mass Transference Principle: Ten Years OnApr 21 2017May 09 2017In this article we discuss the Mass Transference Principle due to Beresnevich and Velani and survey several generalisations and variants, both deterministic and random. Using a Hausdorff measure analogue of the inhomogeneous Khintchine-Groshev Theorem, ... More
Unsupervised Predictive Memory in a Goal-Directed AgentMar 28 2018Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has ... More
DeepMind LabDec 12 2016Dec 13 2016DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, ... More
Human-level performance in first-person multiplayer games with population-based deep reinforcement learningJul 03 2018Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and ... More
Parallel WaveNet: Fast High-Fidelity Speech SynthesisNov 28 2017The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential ... More
Distribution Of Sequences Generated By Certain Simply-Constructed Normal NumbersNov 04 2015In 1949 Wall showed that $x = 0.d_1d_2d_3 \dots$ is normal if and only if $(0.d_nd_{n+1}d_{n+2} \dots)_n$ is a uniformly distributed sequence. In this article, we consider sequences which are slight variants on this. In particular, we show that certain ... More
Dynamic Backward Slicing of Rewriting Logic ComputationsMay 13 2011Jun 06 2011Trace slicing is a widely used technique for execution trace analysis that is effectively used in program debugging, analysis and comprehension. In this paper, we present a backward trace slicing technique that can be used for the analysis of Rewriting ... More
Debugging of Web Applications with Web-TLRAug 11 2011Web-TLR is a Web verification engine that is based on the well-established Rewriting Logic--Maude/LTLR tandem for Web system specification and model-checking. In Web-TLR, Web applications are expressed as rewrite theories that can be formally verified ... More
Latent Alignment and Variational AttentionJul 10 2018Nov 07 2018Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method for softly simulating alignment; however, the approach does not marginalize ... More
Evaluating Compositionality in Sentence EmbeddingsFeb 12 2018May 17 2018An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to other tasks. We present a new ... More