390 citations · 416 across the 19 of their papers we have counts for
19 papers
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
Niharika S. D'Souza, Archana Venkataraman
Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectom…
A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion
Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon +1
This paper introduces a new framework for non-parallel emotion conversion in speech. Our framework is based on two key contributions. First, we propose a stochastic version of the…
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition
Ravi Shankar, Abdouh Harouna Kenfack, Arjun Somayazulu +1
Automated emotion recognition in speech is a long-standing problem. While early work on emotion recognition relied on hand-crafted features and simple classifiers, the field has no…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
A Deep-Bayesian Framework for Adaptive Speech Duration Modification
Ravi Shankar, Archana Venkataraman
We propose the first method to adaptively modify the duration of a given speech signal. Our approach uses a Bayesian framework to define a latent attention map that links frames of…
A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by su…
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
Sayan Ghosal, Qiang Chen, Giulio Pergola +10
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an…
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
Naresh Nandakumar, Niharika Shimona D'souza, Komal Manzoor +4
We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor p…
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes th…
Multi-speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network
Ravi Shankar, Hsi-Wei Hsieh, Nicolas Charon +1
We propose a novel method for emotion conversion in speech based on a chained encoder-decoder-predictor neural network architecture. The encoder constructs a latent embedding of th…
Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator
Ravi Shankar, Jacob Sager, Archana Venkataraman
We introduce a novel method for emotion conversion in speech that does not require parallel training data. Our approach loosely relies on a cycle-GAN schema to minimize the reconst…
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tr…
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
The problem of linking functional connectomics to behavior is extremely challenging due to the complex interactions between the two distinct, but related, data domains. We propose…
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4
We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imag…
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral…
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
Jeff Craley, Emily Johnson, Archana Venkataraman
We propose a novel Coupled Hidden Markov Model to detect epileptic seizures in multichannel electroencephalography (EEG) data. Our model defines a network of seizure propagation pa…
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2
We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subne…
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
Nicha C. Dvornek, Daniel Yang, Archana Venkataraman +4
Treating children with autism spectrum disorders (ASD) with behavioral interventions, such as Pivotal Response Treatment (PRT), has shown promise in recent studies. However, decidi…