Search Results for author:
Found 23 papers, 10 papers with code
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.
Ranked #30 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Faithful Reasoning Using Large Language Models
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model.
Language models show human-like content effects on reasoning tasks
We evaluate state of the art large language models, as well as humans, and find that the language models reflect many of the same patterns observed in humans across these tasks $\unicode{x2014}$ like humans, models answer more accurately when the semantic content of a task supports the logical inferences.
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks.
Can language models learn from explanations in context?
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Leveraging the shared structure that exists across different scenes, our model learns to infer two sets of latent representations from RGB video input alone: a set of "object" latents, corresponding to the time-invariant, object-level contents of the scene, as well as a set of "frame" latents, corresponding to global time-varying elements such as viewpoint.
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
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 across time, and predicts how those objects evolve over successive frames.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
AlignNet: Self-supervised Alignment Module
The natural world consists of objects that we perceive as persistent in space and time, even though these objects appear, disappear and reappear in our field of view as we move.
An Explicitly Relational Neural Network Architecture
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 with an explicitly relational structure from raw pixel data.
Inverting The Generator Of A Generative Adversarial Network (II)
Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss.
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
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 limited supply.
LatentPoison -- Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
Adversarial Information Factorization
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.
LatentPoison - Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.)
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.
On denoising autoencoders trained to minimise binary cross-entropy
When training autoencoders on image data a natural choice of loss function is BCE, since pixel values may be normalised to take values in [0, 1] and the decoder model may be designed to generate samples that take values in (0, 1).
Denoising Adversarial Autoencoders
Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.
Inverting The Generator Of A Generative Adversarial Network
When the high-dimensional distribution describes images of a particular data set, the network should learn to generate visually similar image samples for latent variables that are close to each other in the latent space.
Improving Sampling from Generative Autoencoders with Markov Chains
Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model.
Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpose of the model.
Adversarial Training For Sketch Retrieval
Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification.
