Search Results for author: José Luis Ambite

Found 5 papers, 0 papers with code

Towards Sparsified Federated Neuroimaging Models via Weight Pruning

no code implementations24 Aug 2022 Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar, Greg Ver Steeg, Paul Thompson, José Luis Ambite

In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.

Federated Learning

Federated Named Entity Recognition

no code implementations28 Mar 2022 Joel Mathew, Dimitris Stripelis, José Luis Ambite

We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER).

Federated Learning named-entity-recognition +2

Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

no code implementations10 Dec 2021 Yijun Lin, Yao-Yi Chiang, Meredith Franklin, Sandrah P. Eckel, José Luis Ambite

In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations.

feature selection Time Series +1

Membership Inference Attacks on Deep Regression Models for Neuroimaging

no code implementations6 May 2021 Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.

Federated Learning regression

Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping

no code implementations1 Jun 2019 Joel Mathew, Shobeir Fakhraei, José Luis Ambite

Second, we use a reference set of entity names (e. g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.

Data Augmentation named-entity-recognition +2

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