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08/03/2023
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
Segmentation masks of pathological areas are useful in many medical appl...
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07/25/2023
QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models
Image quality remains a key problem for both traditional and deep learni...
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07/24/2023
Label Noise: Correcting a Correction
Training neural network classifiers on datasets with label noise poses a...
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07/03/2023
Efficient and fully-automatic retinal choroid segmentation in OCT through DL-based distillation of a hand-crafted pipeline
Retinal vascular phenotypes, derived from low-cost, non-invasive retinal...
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05/30/2023
Class Conditional Gaussians for Continual Learning
Dealing with representation shift is one of the main problems in online ...
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03/27/2023
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
In some medical imaging tasks and other settings where only small parts ...
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01/30/2023
Contrastive Meta-Learning for Partially Observable Few-Shot Learning
Many contrastive and meta-learning approaches learn representations by i...
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07/12/2022
Robust and efficient computation of retinal fractal dimension through deep approximation
A retinal trait, or phenotype, summarises a specific aspect of a retinal...
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07/05/2022
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Successful deployment of multi-agent reinforcement learning often requir...
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03/11/2022
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions
Ultra-widefield (UWF) imaging is a promising modality that captures a la...
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03/10/2022
Prediction-Guided Distillation for Dense Object Detection
Real-world object detection models should be cheap and accurate. Knowled...
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12/17/2021
Global explainability in aligned image modalities
Deep learning (DL) models are very effective on many computer vision pro...
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12/02/2021
Hamiltonian prior to Disentangle Content and Motion in Image Sequences
We present a deep latent variable model for high dimensional sequential ...
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06/20/2021
Better Training using Weight-Constrained Stochastic Dynamics
We employ constraints to control the parameter space of deep neural netw...
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04/12/2021
How Sensitive are Meta-Learners to Dataset Imbalance?
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot ...
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01/07/2021
Few-Shot Learning with Class Imbalance
Few-shot learning aims to train models on a limited number of labeled sa...
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11/19/2020
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks
Collider bias is a harmful form of sample selection bias that neural net...
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07/15/2020
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
Gradient-based hyperparameter optimization is an attractive way to perfo...
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06/17/2020
Constraint-Based Regularization of Neural Networks
We propose a method for efficiently incorporating constraints into a sto...
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06/17/2020
Optimizing Grouped Convolutions on Edge Devices
When deploying a deep neural network on constrained hardware, it is poss...
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06/10/2020
Self-Supervised Relational Reasoning for Representation Learning
In self-supervised learning, a system is tasked with achieving a surroga...
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06/08/2020
Neural Architecture Search without Training
The time and effort involved in hand-designing deep neural networks is i...
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04/11/2020
Meta-Learning in Neural Networks: A Survey
The field of meta-learning, or learning-to-learn, has seen a dramatic ri...
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03/13/2020
DHOG: Deep Hierarchical Object Grouping
Recently, a number of competitive methods have tackled unsupervised repr...
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03/13/2020
What Information Does a ResNet Compress?
The information bottleneck principle (Shwartz-Ziv Tishby, 2017) sugg...
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02/20/2020
Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and ...
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02/20/2020
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs
Convolutional Neural Networks (CNN) are becoming a common presence in ma...
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10/11/2019
Deep Kernel Transfer in Gaussian Processes for Few-shot Learning
Humans tackle new problems by making inferences that go far beyond the i...
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06/10/2019
BlockSwap: Fisher-guided Block Substitution for Network Compression
The desire to run neural networks on low-capacity edge devices has led t...
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06/03/2019
Separable Layers Enable Structured Efficient Linear Substitutions
In response to the development of recent efficient dense layers, this pa...
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05/24/2019
Learning to learn by Self-Critique
In few-shot learning, a machine learning system learns from a small set ...
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05/23/2019
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Performing knowledge transfer from a large teacher network to a smaller ...
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02/26/2019
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
The field of few-shot learning has been laboriously explored in the supe...
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11/01/2018
Dilated DenseNets for Relational Reasoning
Despite their impressive performance in many tasks, deep neural networks...
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10/30/2018
Exploration by Random Network Distillation
We introduce an exploration bonus for deep reinforcement learning method...
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10/24/2018
HAKD: Hardware Aware Knowledge Distillation
Despite recent developments, deploying deep neural networks on resource ...
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10/24/2018
Distilling with Performance Enhanced Students
The task of accelerating large neural networks on general purpose hardwa...
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10/22/2018
How to train your MAML
The field of few-shot learning has recently seen substantial advancement...
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10/10/2018
Pruning neural networks: is it time to nip it in the bud?
Pruning is a popular technique for compressing a neural network: a large...
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09/19/2018
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are extremely computationally deman...
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08/13/2018
Large-Scale Study of Curiosity-Driven Learning
Reinforcement learning algorithms rely on carefully engineering environm...
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07/13/2018
DNN's Sharpest Directions Along the SGD Trajectory
Recent work has identified that using a high learning rate or a small ba...
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11/13/2017
Three Factors Influencing Minima in SGD
We study the properties of the endpoint of stochastic gradient descent (...
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11/12/2017
Data Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data. In the low-dat...
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11/07/2017
Moonshine: Distilling with Cheap Convolutions
Model distillation compresses a trained machine learning model, such as ...
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06/07/2016
Towards a Neural Statistician
An efficient learner is one who reuses what they already know to tackle ...
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11/18/2015
Censoring Representations with an Adversary
In practice, there are often explicit constraints on what representation...
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12/10/2014
Teaching Deep Convolutional Neural Networks to Play Go
Mastering the game of Go has remained a long standing challenge to the f...
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03/04/2014
Multi-period Trading Prediction Markets with Connections to Machine Learning
We present a new model for prediction markets, in which we use risk meas...
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09/27/2013
