842 research outputs found
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?
Research in machine learning for autism spectrum disorder (ASD)
classification bears the promise to improve clinical diagnoses. However, recent
studies in clinical imaging have shown the limited generalization of biomarkers
across and beyond benchmark datasets. Despite increasing model complexity and
sample size in neuroimaging, the classification performance of ASD remains far
away from clinical application. This raises the question of how we can overcome
these barriers to develop early biomarkers for ASD. One approach might be to
rethink how we operationalize the theoretical basis of this disease in machine
learning models. Here we introduced unsupervised graph representations that
explicitly map the neural mechanisms of a core aspect of ASD, deficits in
dyadic social interaction, as assessed by dual brain recordings, termed
hyperscanning, and evaluated their predictive performance. The proposed method
differs from existing approaches in that it is more suitable to capture social
interaction deficits on a neural level and is applicable to young children and
infants. First results from functional near-infrared spectroscopy data indicate
potential predictive capacities of a task-agnostic, interpretable graph
representation. This first effort to leverage interaction-related deficits on
neural level to classify ASD may stimulate new approaches and methods to
enhance existing models to achieve developmental ASD biomarkers in the future.Comment: Accepted in Medical Image Computing and Computer Assisted
Intervention - MICCAI 2022: The 5th International Workshop on Machine
Learning in Clinical Neuroimagin
A Generalist Framework for Panoptic Segmentation of Images and Videos
Panoptic segmentation assigns semantic and instance ID labels to every pixel
of an image. As permutations of instance IDs are also valid solutions, the task
requires learning of high-dimensional one-to-many mapping. As a result,
state-of-the-art approaches use customized architectures and task-specific loss
functions. We formulate panoptic segmentation as a discrete data generation
problem, without relying on inductive bias of the task. A diffusion model is
proposed to model panoptic masks, with a simple architecture and generic loss
function. By simply adding past predictions as a conditioning signal, our
method is capable of modeling video (in a streaming setting) and thereby learns
to track object instances automatically. With extensive experiments, we
demonstrate that our simple approach can perform competitively to
state-of-the-art specialist methods in similar settings.Comment: ICCV'23. Code at https://github.com/google-research/pix2se
Differentially Private Mixture of Generative Neural Networks
Generative models are used in a wide range of applications building on large
amounts of contextually rich information. Due to possible privacy violations of
the individuals whose data is used to train these models, however, publishing
or sharing generative models is not always viable. In this paper, we present a
novel technique for privately releasing generative models and entire
high-dimensional datasets produced by these models. We model the generator
distribution of the training data with a mixture of generative neural
networks. These are trained together and collectively learn the generator
distribution of a dataset. Data is divided into clusters, using a novel
differentially private kernel -means, then each cluster is given to separate
generative neural networks, such as Restricted Boltzmann Machines or
Variational Autoencoders, which are trained only on their own cluster using
differentially private gradient descent. We evaluate our approach using the
MNIST dataset, as well as call detail records and transit datasets, showing
that it produces realistic synthetic samples, which can also be used to
accurately compute arbitrary number of counting queries.Comment: A shorter version of this paper appeared at the 17th IEEE
International Conference on Data Mining (ICDM 2017). This is the full
version, published in IEEE Transactions on Knowledge and Data Engineering
(TKDE
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