research
∙
11/25/2022
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning ...
share
research
∙
08/30/2022
Faithful Reasoning Using Large Language Models
Although contemporary large language models (LMs) demonstrate impressive...
research
∙
07/14/2022
Language models show human-like content effects on reasoning
Abstract reasoning is a key ability for an intelligent system. Large lan...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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 ...
research
∙
07/17/2020
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment sce...
research
∙
05/24/2019
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, w...
research
∙
02/15/2018
Inverting The Generator Of A Generative Adversarial Network (II)
Generative adversarial networks (GANs) learn a deep generative model tha...
research
∙
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...
research
∙
11/14/2017
Conditional Autoencoders with Adversarial Information Factorization
Generative models, such as variational auto-encoders (VAE) and generativ...
research
∙
11/08/2017
LatentPoison - Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined...
research
∙
10/19/2017
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep repre...
research
∙
08/28/2017
On denoising autoencoders trained to minimise binary cross-entropy
Denoising autoencoders (DAEs) are powerful deep learning models used for...
research
∙
03/03/2017
Denoising Adversarial Autoencoders
Unsupervised learning is of growing interest because it unlocks the pote...
research
∙
11/17/2016
Inverting The Generator Of A Generative Adversarial Network
Generative adversarial networks (GANs) learn to synthesise new samples f...
research
∙
10/28/2016
Improving Sampling from Generative Autoencoders with Markov Chains
We focus on generative autoencoders, such as variational or adversarial ...
research
∙
09/27/2016
Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpos...
research
∙
07/10/2016
