Search Results for author: Karl Moritz Hermann

Found 32 papers, 14 papers with code

Learning to encode spatial relations from natural language

no code implementations ICLR 2019 Tiago Ramalho, Tomas Kocisky‎, Frederic Besse, S. M. Ali Eslami, Gabor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

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.

Natural Language Processing

The StreetLearn Environment and Dataset

1 code implementation4 Mar 2019 Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Denis Teplyashin, Karl Moritz Hermann, Mateusz Malinowski, Matthew Koichi Grimes, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell

These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported.

Decision Making

Learning To Follow Directions in Street View

1 code implementation1 Mar 2019 Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision.

Encoding Spatial Relations from Natural Language

2 code implementations4 Jul 2018 Tiago Ramalho, Tomáš Kočiský, Frederic Besse, S. M. Ali Eslami, Gábor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann

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.

Natural Language Processing

Hyperbolic Attention Networks

no code implementations ICLR 2019 Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas

We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure.

Machine Translation Question Answering +2

Pushing the bounds of dropout

1 code implementation ICLR 2019 Gábor Melis, Charles Blundell, Tomáš Kočiský, Karl Moritz Hermann, Chris Dyer, Phil Blunsom

We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective.

Language Modelling

Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

no code implementations ICLR 2018 Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark

The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.

reinforcement-learning

Learning to Navigate in Cities Without a Map

3 code implementations NeurIPS 2018 Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell

We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.

Autonomous Navigation reinforcement-learning

Understanding Grounded Language Learning Agents

no code implementations ICLR 2018 Felix Hill, Karl Moritz Hermann, Phil Blunsom, Stephen Clark

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds.

Grounded language learning Policy Gradient Methods

The NarrativeQA Reading Comprehension Challenge

1 code implementation TACL 2018 Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette

Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document.

Ranked #9 on Question Answering on NarrativeQA (BLEU-1 metric)

Information Retrieval Question Answering +1

Understanding Early Word Learning in Situated Artificial Agents

no code implementations ICLR 2018 Felix Hill, Stephen Clark, Karl Moritz Hermann, Phil Blunsom

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds.

Grounded language learning Policy Gradient Methods

Grounded Language Learning in a Simulated 3D World

1 code implementation20 Jun 2017 Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom

Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.

Grounded language learning

Reasoning about Entailment with Neural Attention

7 code implementations22 Sep 2015 Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom

We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases.

Natural Language Inference Natural Language Processing

Learning to Transduce with Unbounded Memory

4 code implementations NeurIPS 2015 Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom

Recently, strong results have been demonstrated by Deep Recurrent Neural Networks on natural language transduction problems.

Natural Language Transduction Translation

Deep Learning for Answer Sentence Selection

2 code implementations4 Dec 2014 Lei Yu, Karl Moritz Hermann, Phil Blunsom, Stephen Pulman

Answer sentence selection is the task of identifying sentences that contain the answer to a given question.

Feature Engineering Open-Domain Question Answering

Distributed Representations for Compositional Semantics

no code implementations12 Nov 2014 Karl Moritz Hermann

The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.

Natural Language Processing Representation Learning

Learning Bilingual Word Representations by Marginalizing Alignments

no code implementations ACL 2014 Tomáš Kočiský, Karl Moritz Hermann, Phil Blunsom

We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data.

General Classification

A Deep Architecture for Semantic Parsing

no code implementations WS 2014 Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann

Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries.

Semantic Parsing

"Not not bad" is not "bad": A distributional account of negation

no code implementations10 Jun 2013 Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom

With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing.

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