Publications (17)
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
Christopher Beckham, Martin Weiss, Florian Golemo +3
Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene…
Conservative objective models are a special kind of contrastive divergence-based energy model
Christopher Beckham, Christopher Pal
In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind of contrastive divergence-based energy mo…
Overcoming challenges in leveraging GANs for few-shot data augmentation
Christopher Beckham, Issam Laradji, Pau Rodriguez +3
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform an exploration into how a GAN can b…
Unimodal probability distributions for deep ordinal classification
Christopher Beckham, Christopher Pal
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constra…
On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma +5
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the a…
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…
Exploring validation metrics for offline model-based optimisation with diffusion models
Christopher Beckham, Alexandre Piche, David Vazquez +1
In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called…
Parallel-mentoring for Offline Model-based Optimization
Can Chen, Christopher Beckham, Zixuan Liu +2
We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These designs encompass a variety of domains, incl…
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Evan Racah, Christopher Beckham, Tegan Maharaj +3
Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions…
Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards
Luis Lara, Aristides Milios, Zhi Hao Luo +5
An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between rooms and maintaining functional…
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham +5
Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes di…
DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan Design
Zhi Hao Luo, Luis Lara, Ge Ya Luo +3
Text conditioned generative models for images have yielded impressive results. Text conditioned floorplan generation as a special type of raster image generation task also received…
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte +2
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that mat…
A step towards procedural terrain generation with GANs
Christopher Beckham, Christopher Pal
Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step to…
A simple squared-error reformulation for ordinal classification
Christopher Beckham, Christopher Pal
In this paper, we explore ordinal classification (in the context of deep neural networks) through a simple modification of the squared error loss which not only allows it to not on…
Robust Guided Diffusion for Offline Black-Box Optimization
Can Sam Chen, Christopher Beckham, Zixuan Liu +2
Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forwar…
Towards annotation-efficient segmentation via image-to-image translation
Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham +2
Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use o…