Deniz Erdogmus

research

∙ 05/12/2022

Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are us...

Nasim Soltani, et al. ∙

research

∙ 05/02/2022

Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

Fetal brain segmentation is an important first step for slice-level moti...

Razieh Faghihpirayesh, et al. ∙

research

∙ 04/13/2022

Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering

In this paper we present a hybrid neural network augmented physics-based...

Tales Imbiriba, et al. ∙

research

∙ 12/17/2021

AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data

We provide a regularization framework for subject transfer learning in w...

Niklas Smedemark-Margulies, et al. ∙

research

∙ 11/02/2021

Equivariant Deep Dynamical Model for Motion Prediction

Learning representations through deep generative modeling is a powerful ...

Bahar Azari, et al. ∙

research

∙ 10/12/2021

Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models

Modeling biological dynamical systems is challenging due to the interdep...

Ahmet Demirkaya, et al. ∙

research

∙ 10/03/2021

Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules

Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very ...

Paul Ghanem, et al. ∙

research

∙ 07/26/2021

Circular-Symmetric Correlation Layer based on FFT

Despite the vast success of standard planar convolutional neural network...

Bahar Azari, et al. ∙

research

∙ 06/16/2021

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals

Convolutional neural networks (CNN) have been frequently used to extract...

Andac Demir, et al. ∙

research

∙ 05/04/2021

On the Sample Complexity of Rank Regression from Pairwise Comparisons

We consider a rank regression setting, in which a dataset of N samples w...

Berkan Kadioglu, et al. ∙

research

∙ 05/01/2021

Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks

Feature ranking and selection is a widely used approach in various appli...

Ozan Özdenizci, et al. ∙

research

∙ 04/26/2021

End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

State-of-the-art human-in-the-loop robot grasping is hugely suffered by ...

Mohammadreza Sharif, et al. ∙

research

∙ 04/19/2021

Segmentation and Classification of EMG Time-Series During Reach-to-Grasp Motion

The electromyography (EMG) signals have been widely utilized in human ro...

Mo Han, et al. ∙

research

∙ 04/17/2021

Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing

Autoencoder (AEC) networks have recently emerged as a promising approach...

Haoqing Li, et al. ∙

research

∙ 04/08/2021

Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control

For lower arm amputees, robotic prosthetic hands offer the promise to re...

Mehrshad Zandigohar, et al. ∙

research

∙ 02/17/2021

EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks

During daily activities, humans use their hands to grasp surrounding obj...

Ozan Özdenizci, et al. ∙

research

∙ 02/16/2021

On the use of generative deep neural networks to synthesize artificial multichannel EEG signals

Recent promises of generative deep learning lately brought interest to i...

Ozan Özdenizci, et al. ∙

research

∙ 01/13/2021

NetCut: Real-Time DNN Inference Using Layer Removal

Deep Learning plays a significant role in assisting humans in many aspec...

Mehrshad Zandigohar, et al. ∙

research

∙ 01/13/2021

Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand

For lower arm amputees, prosthetic hands promise to restore most of phys...

Mehrshad Zandigohar, et al. ∙

research

∙ 09/28/2020

Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders

Human computer interaction (HCI) involves a multidisciplinary fusion of ...

Mo Han, et al. ∙

research

∙ 08/26/2020

Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction

Recent developments in biosignal processing have enabled users to exploi...

Mo Han, et al. ∙

research

∙ 07/30/2020

Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry

Systems that are based on recursive Bayesian updates for classification ...

Aziz Koçanaoğulları, et al. ∙

research

∙ 07/02/2020

AutoBayes: Automated Inference via Bayesian Graph Exploration for Nuisance-Robust Biosignal Analysis

Learning data representations that capture task-related features, but ar...

Andac Demir, et al. ∙

research

∙ 04/30/2020

Prediction of Epilepsy Development in Traumatic Brain Injury Patients from Diffusion Weighted MRI

Post-traumatic epilepsy (PTE) is a life-long complication of traumatic b...

Md Navid Akbar, et al. ∙

research

∙ 04/15/2020

Disentangled Adversarial Transfer Learning for Physiological Biosignals

Recent developments in wearable sensors demonstrate promising results fo...

Mo Han, et al. ∙

research

∙ 04/07/2020

Active Recursive Bayesian Inference with Posterior Trajectory Analysis Using α-Divergence

Recursive Bayesian inference (RBI) provides optimal Bayesian latent vari...

Yeganeh M. Marghi, et al. ∙

research

∙ 02/14/2020

Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach

A deep neural network (DNN) that can reliably model muscle responses fro...

Md Navid Akbar, et al. ∙

research

∙ 10/02/2019

User-Adaptive Text Entry for Augmentative and Alternative Communication

The viability of an Augmentative and Alternative Communication device of...

Matt Higger, et al. ∙

research

∙ 07/22/2019

Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects

Across- and within-recording variabilities in electroencephalographic (E...

Ozan Özdenizci, et al. ∙

research

∙ 03/28/2019

Information Theoretic Feature Transformation Learning for Brain Interfaces

Objective: A variety of pattern analysis techniques for model training i...

Ozan Özdenizci, et al. ∙

research

∙ 03/27/2019

Adversarial Deep Learning in EEG Biometrics

Deep learning methods for person identification based on electroencephal...

Ozan Özdenizci, et al. ∙

research

∙ 01/18/2019

Accelerated Experimental Design for Pairwise Comparisons

Pairwise comparison labels are more informative and less variable than c...

Yuan Guo, et al. ∙

research

∙ 12/17/2018

Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

We introduce adversarial neural networks for representation learning as ...

Ozan Özdenizci, et al. ∙

research

∙ 09/15/2018

Time-Series Prediction of Proximal Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Physiological Biosignals

It has been suggested that changes in physiological arousal precede pote...

Ozan Özdenizci, et al. ∙

research

∙ 09/15/2018

Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

We present a novel hierarchical graphical model based context-aware hybr...

Ozan Özdenizci, et al. ∙

research

∙ 08/05/2018

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNN...

Kaidi Xu, et al. ∙

research

∙ 05/21/2018

Invariant Representations from Adversarially Censored Autoencoders

We combine conditional variational autoencoders (VAE) with adversarial c...

Ye Wang, et al. ∙

research

∙ 03/28/2018

Tversky as a Loss Function for Highly Unbalanced Image Segmentation using 3D Fully Convolutional Deep Networks

Fully convolutional deep neural networks have been asserted to be fast a...

Seyed Raein Hashemi, et al. ∙

research

∙ 03/15/2018

Real-time Deep Registration With Geodesic Loss

With an aim to increase the capture range and accelerate the performance...

Seyed Sadegh Mohseni Salehi, et al. ∙

research

∙ 10/25/2017

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

Brain segmentation is a fundamental first step in neuroimage analysis. I...

Seyed Sadegh Mohseni Salehi, et al. ∙

research

∙ 06/18/2017

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential f...

Seyed Sadegh Mohseni Salehi, et al. ∙

research

∙ 03/06/2017

Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

Brain extraction or whole brain segmentation is an important first step ...

Seyed Sadegh Mohseni Salehi, et al. ∙

research

∙ 05/27/2016

Asymptotic Analysis of Objectives based on Fisher Information in Active Learning

Obtaining labels can be costly and time-consuming. Active learning allow...

Jamshid Sourati, et al. ∙

research

∙ 04/06/2016

Manifold unwrapping using density ridges

Research on manifold learning within a density ridge estimation framewor...

Jonas Nordhaug Myhre, et al. ∙

Deniz Erdogmus

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