Tomás Kociský

Publications
Influence

Teaching Machines to Read and Comprehend

K. Hermann, Tomás Kociský, +4 authors P. Blunsom
Computer Science
NIPS
10 June 2015

We develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure. Expand

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Reasoning about Entailment with Neural Attention

Tim Rocktäschel, Edward Grefenstette, K. Hermann, Tomás Kociský, P. Blunsom
Computer Science
ICLR
22 September 2015

In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. Expand

The NarrativeQA Reading Comprehension Challenge

Tomás Kociský, Jonathan Schwarz, +4 authors Edward Grefenstette
Computer Science
Transactions of the Association for Computational…
19 December 2017

We present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. Expand

Latent Predictor Networks for Code Generation

W. Ling, P. Blunsom, +4 authors A. Senior
Computer Science
ACL
22 March 2016

We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Expand

Optimizing Performance of Recurrent Neural Networks on GPUs

J. Appleyard, Tomás Kociský, P. Blunsom
Computer Science
ArXiv
7 April 2016

In this article we demonstrate that by exposing parallelism between operations within the network, an order of magnitude speedup across a range of network sizes can be achieved over a naive implementation. Expand

Semantic Parsing with Semi-Supervised Sequential Autoencoders

Tomás Kociský, Gábor Melis, +4 authors K. Hermann
Computer Science
EMNLP
29 September 2016

We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing and extend those datasets with synthetically generated logical forms. Expand

Learning Bilingual Word Representations by Marginalizing Alignments

Tomás Kociský, K. Hermann, P. Blunsom
Computer Science
ACL
5 May 2014

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

Learning and Evaluating General Linguistic Intelligence

Dani Yogatama, Cyprien de Masson d'Autume, +8 authors P. Blunsom
Computer Science, Mathematics
ArXiv
31 January 2019

We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks. Expand

The Neural Noisy Channel

L. Yu, P. Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský
Computer Science
ICLR
4 November 2016

We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models to obtain a tractable and effective beam search decoder. Expand

Aggregation and Ordering in Factorised Databases

Nurzhan Bakibayev, Tomás Kociský, D. Olteanu, Jakub Závodný
Computer Science
Proc. VLDB Endow.
1 July 2013

In this paper, we extend FDB to support a larger class of practical queries with aggregates and ordering, while still maintaining its performance superiority over relational query techniques. Expand

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