4.6k citations · 5.3k across the 20 of their papers we have counts for
21 papers
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar +6
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question o…
Faithful Reasoning Using Large Language Models
Antonia Creswell, Murray Shanahan
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. Thi…
Language models show human-like content effects on reasoning tasks
Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan +5
Reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. Howe…
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Antonia Creswell, Murray Shanahan, Irina Higgins
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reas…
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan +6
Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We the…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Rishabh Kabra, Daniel Zoran, Goker Erdogan +5
To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and charac…
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
Antonia Creswell, Rishabh Kabra, Chris Burgess +1
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order a…
AlignNet: Unsupervised Entity Alignment
Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals +8
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowi…
An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell +3
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations w…
Inverting The Generator Of A Generative Adversarial Network (II)
Antonia Creswell, Anil A Bharath
Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing la…
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
Antonia Creswell, Alison Pouplin, Anil A Bharath
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in lim…
LatentPoison - Adversarial Attacks On The Latent Space
Antonia Creswell, Anil A. Bharath, Biswa Sengupta
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide vari…
Adversarial Information Factorization
Antonia Creswell, Yumnah Mohamied, Biswa Sengupta +1
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single objec…
Generative Adversarial Networks: An Overview
Antonia Creswell, Tom White, Vincent Dumoulin +3
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation s…
On denoising autoencoders trained to minimise binary cross-entropy
Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which…
Denoising Adversarial Autoencoders
Antonia Creswell, Anil Anthony Bharath
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a…
Inverting The Generator Of A Generative Adversarial Network
Antonia Creswell, Anil Anthony Bharath
Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative networ…
Improving Sampling from Generative Autoencoders with Markov Chains
Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders a…
Task Specific Adversarial Cost Function
Antonia Creswell, Anil A. Bharath
The cost function used to train a generative model should fit the purpose of the model. If the model is intended for tasks such as generating perceptually correct samples, it is be…
Adversarial Training For Sketch Retrieval
Antonia Creswell, Anil Anthony Bharath
Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representat…