Search Results for author:
Found 9 papers, 4 papers with code
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models; however, modern deterministic deep learning (DL) models are often poorly calibrated and lack the ability to communicate a measure of epistemic uncertainty in prediction, which is desirable for remote sensing platforms and safety-critical applications.
Concrete Safety for ML Problems: System Safety for ML Development and Assessment
Many stakeholders struggle to make reliances on ML-driven systems due to the risk of harm these systems may cause.
VICE: Variational Interpretable Concept Embeddings
This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.
A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes.
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness
We introduce two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data.
Knowing what you know in brain segmentation using Bayesian deep neural networks
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns.
Robustly representing uncertainty in deep neural networks through sampling
We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10.
Representational Distance Learning for Deep Neural Networks
We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher.
