Search Results for author:
Found 17 papers, 5 papers with code
Model Architecture Adaption for Bayesian Neural Networks
Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to quantify the uncertainty of model predictions but come with a prohibitive computation cost for both training and inference.
Fine-grained Multi-Modal Self-Supervised Learning
Multi-Modal Self-Supervised Learning from videos has been shown to improve model's performance on various downstream tasks.
Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices
PRD reframes the RPM problem into a relation comparison task, which we can solve without requiring the labelling of the RPM problem.
Learned Low Precision Graph Neural Networks
LPGNAS learns the optimal architecture coupled with the best quantisation strategy for different components in the GNN automatically using back-propagation in a single search round.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
Top-Related Meta-Learning Method for Few-Shot Object Detection
Therefore, based on semantic features, we propose a Top-C classification loss (i. e., TCL-C) for classification task and a category-based grouping mechanism for category-based meta-features obtained by the meta-model.
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM).
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
We show that neural nets with this inductive bias achieve considerably better o. o. d generalisation performance for a range of relational reasoning tasks.
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs).
Are Registration Uncertainty and Error Monotonically Associated
Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error.
Mixed-Supervised Dual-Network for Medical Image Segmentation
The model is trained jointly in a multi-task learning setting.
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes.
Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat
In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a Recurrent Auto-Encoder with structured latent distributions containing discrete categorical distributions, continuous attribute distributions, and factorised spatial attention.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Methods of parameter pruning and quantization are described first, after that the other techniques are introduced.
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets
In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks).
