Antonia Creswell | DeepAI

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Oriol Vinyals
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∙ 11/25/2022

Solving math word problems with process- and outcome-based feedback

Recent work has shown that asking language models to generate reasoning ...

0 Jonathan Uesato, et al. ∙

∙ 08/30/2022

Faithful Reasoning Using Large Language Models

Although contemporary large language models (LMs) demonstrate impressive...

8 Antonia Creswell, et al. ∙

∙ 07/14/2022

Language models show human-like content effects on reasoning

Abstract reasoning is a key ability for an intelligent system. Large lan...

9 Ishita Dasgupta, et al. ∙

∙ 05/19/2022

Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

Large language models (LLMs) have been shown to be capable of impressive...

28 Antonia Creswell, et al. ∙

∙ 04/05/2022

Can language models learn from explanations in context?

Large language models can perform new tasks by adapting to a few in-cont...

2 Andrew K. Lampinen, et al. ∙

∙ 06/07/2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition

To help agents reason about scenes in terms of their building blocks, we...

0 Rishabh Kabra, et al. ∙

∙ 03/08/2021

Unsupervised Object-Based Transition Models for 3D Partially Observable Environments

We present a slot-wise, object-based transition model that decomposes a ...

2 Antonia Creswell, et al. ∙

∙ 07/17/2020

AlignNet: Unsupervised Entity Alignment

Recently developed deep learning models are able to learn to segment sce...

35 Antonia Creswell, et al. ∙

∙ 05/24/2019

An Explicitly Relational Neural Network Architecture

With a view to bridging the gap between deep learning and symbolic AI, w...

13 Murray Shanahan, et al. ∙

∙ 02/15/2018

Inverting The Generator Of A Generative Adversarial Network (II)

Generative adversarial networks (GANs) learn a deep generative model tha...

0 Antonia Creswell, et al. ∙

∙ 01/02/2018

Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data

We propose a novel deep learning model for classifying medical images in...

0 Antonia Creswell, et al. ∙

∙ 11/14/2017

Conditional Autoencoders with Adversarial Information Factorization

Generative models, such as variational auto-encoders (VAE) and generativ...

0 Antonia Creswell, et al. ∙

∙ 11/08/2017

LatentPoison - Adversarial Attacks On The Latent Space

Robustness and security of machine learning (ML) systems are intertwined...

0 Antonia Creswell, et al. ∙

∙ 10/19/2017

Generative Adversarial Networks: An Overview

Generative adversarial networks (GANs) provide a way to learn deep repre...

0 Antonia Creswell, et al. ∙

∙ 08/28/2017

On denoising autoencoders trained to minimise binary cross-entropy

Denoising autoencoders (DAEs) are powerful deep learning models used for...

0 Antonia Creswell, et al. ∙

∙ 03/03/2017

Denoising Adversarial Autoencoders

Unsupervised learning is of growing interest because it unlocks the pote...

0 Antonia Creswell, et al. ∙

∙ 11/17/2016

Inverting The Generator Of A Generative Adversarial Network

Generative adversarial networks (GANs) learn to synthesise new samples f...

0 Antonia Creswell, et al. ∙

∙ 10/28/2016

Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial ...

0 Antonia Creswell, et al. ∙

∙ 09/27/2016

Task Specific Adversarial Cost Function

The cost function used to train a generative model should fit the purpos...

0 Antonia Creswell, et al. ∙

∙ 07/10/2016

Adversarial Training For Sketch Retrieval

Generative Adversarial Networks (GAN) are able to learn excellent repres...

0 Antonia Creswell, et al. ∙

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